Systems and methods for determining the probability of a pregnancy at a selected point in time

ABSTRACT

The present invention generally relates to systems and methods for determining the probability of a pregnancy at a selected point in time. Systems and methods of the invention employ an algorithm that has been trained on a reference set of data from a plurality of women for whom at least one of fertility-associated phenotypic traits, fertility-associated medical interventions, or pregnancy outcomes are known, in which the algorithm accounts for any woman who ceases pregnancy attempts prior to reaching a live birth outcome.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present patent application is a continuation-in-part of co-pendingU.S. nonprovisional patent application Ser. No. 13/654,082, filed Oct.17, 2012, the content of which is incorporated by reference herein inits entirety.

FIELD OF THE INVENTION

The present invention generally relates to systems and methods fordetermining the probability of a pregnancy at a selected point in time.

BACKGROUND

Approximately one in seven couples has difficulty conceiving.Infertility may be due to a single cause in either partner, or acombination of factors (e.g., genetic factors, diseases, orenvironmental factors) that may prevent a pregnancy from occurring orcontinuing. Every woman will become infertile in her lifetime due tomenopause. On average, egg quality and number begins to declineprecipitously at 35. However, a number of women are fertile well intotheir 40's, while some women experience that decline much earlier inlife. Although advanced maternal age (35 and above) is generallyassociated with poorer fertility outcomes, there is no way of diagnosingegg quality issues in younger women or knowing when a particular womanwill start to experience decline in her egg quality or reserve. When awoman seeks medical assistance for difficulty conceiving, she and herpartner are advised to undergo a number of diagnostic procedures toascertain potential causes. Throughout the process, the couple's mainquestion is whether that treatment will result in a baby.

Predicting a couple's probability of achieving a pregnancy that resultsin a live birth is difficult, and most statistical approaches do notprovide an accurate result, generally overestimating the couple'sprobability of achieving such a pregnancy. That problem is illustratedwith the very common technology of in vitro fertilization (IVF), aprocess in which egg cells are fertilized outside a woman's womb andthen implanted into the womb. Generally, about 52% of couples undergoingIVF do not achieve a pregnancy after a first cycle of treatment, andabout 59% of couples undergoing IVF do not achieve a live birth after afirst cycle of treatment (“2009 Clinic Summary Report”, Society forReproductive Medicine). Accordingly, many couples will undergo at leastone subsequent cycle of IVF, and a percentage of those couples will notachieve a pregnancy or live birth even after numerous IVF cycles.

In IVF, the statistic typically reported to couples is outcome per cycleaccording to maternal age (cross-sectional reporting). For example, aphysician may tell a couple in which the woman is under 35 that theyhave a 30% to 35% probability of achieving a live birth using IVF,meaning that for each cycle of IVF started, there is a 30% to 35%probability that a live birth will be achieved. That statistic is notaccurate because it does not consider the potential need for multipleIVF cycles and the likely difference in success between a first-timepatient and one who did not become pregnant in previous attempts. Thus,using this cross-section reporting approach, a physician overestimates acouple's probability of achieving a pregnancy that results in a livebirth from IVF.

SUMMARY

The invention generally relates to systems and methods for determiningthe probability of a pregnancy at a selected point in time. Generally,aspects of the invention are accomplished by using data from a cohort ofwomen for whom at least one of fertility-associated phenotypic traits,fertility-associated medical interventions, and pregnancy outcomes areknown. A plurality of fertility-associated phenotypic traits of a femalesubject, and optionally an intimate male partner, are obtained and runthrough an algorithm trained by the cohort in order to determine aprobability of pregnancy at a selected point in time using a particularfertility treatment. Accordingly, systems and methods of the inventionprovide a longitudinal analysis that makes use of repeated observationsfrom the cohort over time, providing a better analysis based on thespecific phenotypic traits of that couple in connection with theirchosen medical intervention. In this manner, systems and methods of theinvention are able to more accurately report to a couple whether theselected medical intervention the couple has chosen to undergo willresult in a baby.

Further, systems and methods of the invention also recognize that womenthat have a poor prognosis of achieving a pregnancy or a live birthafter beginning a medical intervention may choose to discontinue thecourse of treatment, i.e., not participate in further rounds oftreatment. Failure to account for the phenotypic traits of those womenleads to overestimating the probability of achieving a pregnancy or alive birth using a particular fertility treatment. Systems and methodsof the invention account for that potential bias by analyzing the knownphenotypic traits of the women from the cohort that have chosen todiscontinue treatment and factor those traits into the analysis.Accordingly, traits from women with a poor prognosis of achieving apregnancy or live birth are accounted for and the probabilities ofachieving pregnancy are adjusted over time. In this manner, bias of thecohort is eliminated and systems and methods of the invention avoidreporting an overly optimistic probability of achieving a pregnancy orlive birth in connection with a particular fertility treatment.

Systems and methods of the invention are useful with all types offertility treatments, and are particularly useful with in vitrofertilization (IVF). In the context of IVF, the invention recognizesthat the chance of achieving a pregnancy or live birth varies per cycleof IVF, and also recognizes that there is a difference in successbetween a first-time patient and one who did not become pregnant inprevious attempts. Aspects of the invention are accomplished by usingdata from a cohort of women for whom fertility-associated phenotypictraits and pregnancy outcomes for each cycle of in vitro fertilizationare known. A plurality of fertility-associated phenotypic traits of afemale subject are obtained and run through an algorithm trained by thecohort in order to determine a probability of pregnancy in a selectedcycle of IVF. Since the fertility-associated phenotypic traits andpregnancy outcomes for each cycle of in vitro fertilization of the womenin the cohort are already known, systems and methods of the inventionare able to report a woman's probability of achieving a pregnancy orlive birth for a selected cycle of IVF that accounts for whether thewoman is a first-time patient or a patient that did not become pregnantor achieve a live birth in previous attempts. Therefore, instead of across-sectional statistic, systems and methods of the invention providea longitudinal analysis that makes use of repeated observations from thecohort over time and provides a better analysis of a woman's historyover multiple IVF cycles. The cumulative pregnancy or live-birth rate isused to determine the probability of achieving a pregnancy or live birthover the entire course of treatment.

Further, the invention recognizes that women from the cohort that have apoor prognosis of achieving a pregnancy or a live birth after a firstunsuccessful cycle of IVF may choose to discontinue IVF, i.e., notparticipate in further IVF cycles. Failure to account for the phenotypictraits of the women with a poor prognosis discontinuing treatment, leadsto reporting a higher probability of achieving a pregnancy or a livebirth in a subsequent cycle of IVF than is actually expected. Systemsand methods of the invention account for that potential bias byanalyzing the known phenotypic traits of the women that have chosen todiscontinue IVF and factoring those traits into the analysis insubsequent IVF cycles. Accordingly, traits from those women areaccounted for in subsequent IVF cycles and the phenotypic make-up of thecohort remains consistent over the subsequent IVF cycles. In thismanner, bias of the cohort is eliminated and systems and methods of theinvention avoid reporting an overly optimistic probability of achievinga pregnancy or live birth in a subsequent IVF cycle.

There are many known fertility-associated phenotypic traits, anycombination of which may be used with systems and methods of theinvention. Exemplary fertility-associated phenotypic traits include age,hormone levels, ovarian antral follicle count, body mass index, andcombinations thereof. Any other fertility-associated traits are alsosuitable for use in accordance with the present invention. Informationregarding the fertility-associated phenotypic traits of the female canbe obtained by any means known in the art. In many cases, suchinformation can be obtained from a questionnaire completed by thesubject that contains questions regarding certain fertility-associatedphenotypic traits. Additional information can be obtained from aquestionnaire completed by the subject's partner and blood relatives.Information can also be obtained from the medical history of thesubject, as well as the medical history of blood relatives and otherfamily members. Additional information can be obtained from the medicalhistory and family medical history of the subject's partner. In othercases, the information can be obtained by analyzing a sample collectedfrom the female subject, reproductive partner(s) of the subject, bloodrelatives of the subject, and a combination thereof. The sample mayinclude human tissue or bodily fluid.

Additionally, it is known that certain genetic regions are associatedwith fertility. The presence of certain mutations in those genes orabnormal expression levels of those genes may indicate fertilityoutcomes. Accordingly, in certain aspects of the invention, genotypicdata is also collected and compared to known genotypic results from thewomen in the cohort to help determine a probability of pregnancy at aparticular point in time using a certain fertility treatment. Genotypedata can be obtained by any methods known in the art, for example, bysequencing at least a portion of a relevant genetic region to determinethe presence or absence of a mutation that is associated withinfertility. Exemplary mutations include, without limitation, a singlenucleotide polymorphism, a deletion, an insertion, an inversion, agenetic rearrangement, a copy number variation, or a combinationthereof.

Certain aspects of the invention are especially amenable forimplementation using a computer. The computer or CPU is able to comparethe data regarding the subject's fertility-associated phenotypic traitsto the reference set of data to thereby provide a probability ofachieving pregnancy. Such systems generally include a central processingunit (CPU) and storage coupled to the CPU. The storage storesinstructions that when executed by the CPU, cause the CPU to accept asinput, data that is representative of a plurality offertility-associated phenotypic traits of a female subject. The executedinstructions also cause the computer to provide a probability ofachieving pregnancy at a certain point in time using a particularfertility treatment as a result of inputting the subject data into analgorithm trained on a reference set of data gathered from a pluralityof women for whom fertility-associated phenotypic traits,fertility-associated medical interventions, and pregnancy outcomes areknown.

In certain embodiments, the reference set is stored at a remote locationseparate from the computer and the computer communicates across anetwork to access the reference set in order to make the determination.In other embodiments, the reference set is stored locally within thecomputer and the computer accesses the reference set within the computerin order to make the determination.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts the data analytics pipeline used to predict outcomes forvarious fertility treatment protocols.

FIG. 2 is a chart depicting the probability of live birth per cycle ofIVF according to observed probabilities and conventional methods ofdetermining the probability.

FIG. 3 is a chart depicting assumptions about the rate of live birthresulting from IVF over time.

FIG. 4 illustrates a system for performing methods of the invention.

FIG. 5 is a process chart depicting the procedural steps for determiningthe probability of a pregnancy in a selected cycle of in vitrofertilization, according to certain embodiments.

FIG. 6 is a listing of the fertility-associated phenotypic traitsconsidered in an IVF study using methods of the present invention.

FIG. 7 is a listing of the fertility-associated phenotypic traitsconsidered in a study of non-ART fertility treatments using methods ofthe present invention.

FIG. 8 is a chart depicting the cumulative probability of live birth asdetermined by methods of the present invention for IVF patientsseparated by score quintile.

FIG. 9 a chart depicting the cumulative probability of live birth asdetermined by methods of the present invention for non-ART fertilitytreatment (RE) patients separated by score quintile.

FIG. 10 is a chart depicting the varying predicted future success ratesof IVF patients staying in a particular study and those who dropped out.

FIG. 11 is a chart depicting the cumulative probability of live birthper cycle of IVF according to optimistic and conservative approximationmethods.

FIG. 12 is a chart depicting the cumulative probability of live birthper cycle of non-ART fertility treatments according to optimistic andconservative approximation methods.

FIG. 13 is a chart depicting the cumulative probability of live birthfor IVF and non-ART fertility treatments combined per cycle of treatmentaccording to optimistic and conservative approximation methods.

FIG. 14 is a chart depicting the cumulative probability of live birthper cycle of IVF according to optimistic and conservative approximationmethods as well as the adjusted probability as determined by methods ofthe invention.

FIG. 15 is a chart depicting the cumulative probability of live birthper cycle of non-ART fertility treatments according to optimistic andconservative approximation methods as well as the adjusted probabilityas determined by methods of the invention.

FIG. 16 is a chart depicting the cumulative probability of live birthfor IVF and non-ART fertility treatments combined according tooptimistic and conservative approximation methods as well as theadjusted probability as determined by methods of the invention.

DETAILED DESCRIPTION

The present invention generally relates to systems and methods fordetermining the probability of achieving pregnancy at a selected pointin time using a particular fertility treatment. Systems and methods ofthe invention are useful with all types of fertility treatmentsreproductive technologies, and are particularly useful with in vitrofertilization (IVF). In addition, it is to be understood that theinvention is equally applicable to the determination of a pregnancy thatresults in a live birth.

Certain aspects of the invention are especially amenable forimplementation using a computer. In those embodiments, systems andmethods of the invention encompass a central processing unit (CPU) andstorage coupled to the CPU. The storage stores instructions that whenexecuted by the CPU, cause the CPU to accept as input data that isrepresentative of a plurality of fertility-associated phenotypic traitsof a female subject. The executed instructions also cause the computerto provide a probability of achieving pregnancy at a selected point intime using a certain fertility treatment as a result of comparing theinput data to a reference set of data gathered from a plurality of womenfor whom fertility-associated phenotypic traits, fertility-associatedmedical interventions, and pregnancy outcomes are known. Systems andmethods of the invention are able to account for any woman from thecohort who ceases attempting to become pregnant prior to reaching a livebirth outcome.

Systems and methods of the invention may be used with all types offertility treatments including assisted reproductive technologies (ART).Suitable assisted reproductive technologies include, without limitation,in vitro fertilization (IVF), zygote intrafallopian transfer (ZIFT),gametic intrafallopian transfer (GIFT), or intracytoplasmic sperminjection (ICSI) paired with one of the methods above, and non-ARTfertility treatments (RE) include ovulation induction protocols withdrugs such as Clomiphene or hormone therapy with or without intrauterineinsemination (IUI) with sperm. In IVF, eggs are removed from the femalesubject, fertilized outside the body, and implanted inside the uterus ofthe female subject. ZIFT is similar to IVF in that eggs are removed andfertilization of the eggs occurs outside the body. In ZIFT, however, theeggs are implanted in the Fallopian tube rather than the uterus. GIFTinvolves transferring eggs and sperm into the female subject's Fallopiantube. Accordingly, fertilization occurs inside the woman's body. InICSI, a single sperm is injected into a mature egg that has removed fromthe body. The embryo is then transferred to the uterus or Fallopiantube. In RE, hormone stimulation is used to improve the woman'sfertility. In general, these fertility-associated medical interventionsare not simply a one-time treatment but often require multiple rounds orcycles of treatment. Therefore, systems and methods of the inventionencompass determining the likelihood of achieving pregnancy at aselected point in time, for example, a selected cycle of treatment.

The disclosed methods are also suitable when the female subjectinterested in having a child is not the one who will carry the baby. Forexample, if a surrogate is used, a couple may wish to know thelikelihood that the surrogate can carry the embryo to live birth.Potential surrogates can include traditional and gestational surrogates.With a traditional surrogate, pregnancy may be achieved throughinsemination alone or through the assisted reproductive technologiesdescribed above, and the surrogate will be biologically related to thechild. With a gestational carrier, eggs are removed from the femalesubject, fertilized with her partner's sperm, and transferred to theuterus of the gestational carrier. The gestational carrier will not begenetically related to the child. Whatever type of surrogate is used,the disclosed methods can also be applied to the surrogate as asecondary female subject.

FIG. 1 depicts the data analytics pipeline used to predict outcomes forvarious fertility treatment protocols. In order to determine theprobability of pregnancy for a female subject as a result of the chosenreproductive technology, aspects of the invention include obtaininginformation regarding the subject's fertility-associated phenotypictraits. Exemplary traits are provided in Table 1 below.

TABLE 1 Phenotypic and environmental variables impacting fertilitysuccess Cholesterol levels on different days of the menstrual cycle Ageof first menses for patient and female blood relatives (e.g. sisters,mother, grandmothers) Age of menopause for female blood relatives (e.g.sisters, mother, grandmothers) Number of previous pregnancies(biochemical/ectopic/clinical/fetal heart beat detected, live birthoutcomes), age at the time, and outcome for patient and female bloodrelatives (e.g. sisters, mother, grandmothers) Diagnosis of PolycysticOvarian Syndrome History of hydrosalpinx or tubal occlusion History ofendometriosis, pelvic pain, or painful periods Cancer history/type ofcancer/treatment/outcome for patient and female blood relatives (e.g.sisters, mother, grandmothers) Age that sexual activity began, currentlevel of sexual activity Smoking history for patient and blood relativesTravel schedule/number of flying hours a year/time difference changes ofmore than 3 hours (Jetlag and Flight-associated Radiation Exposure)Nature of periods (length of menses, length of cycle) Biological age(number of years since first menses) Birth control use Drug use (illegalor legal) Body mass index (current, lowest ever, highest ever) Historyof polyps History of hormonal imbalance History of amenorrhoea Historyof eating disorders Alcohol consumption by patient or blood relativesDetails of mother's pregnancy with patient (i.e. measures of uterineenvironment): any drugs taken, smoking, alcohol, stress levels, exposureto plastics (i.e. Tupperware), composition of diet (see below) Sleeppatterns: number of hours a night, continuous/overall Diet: meat,organic produce, vegetables, vitamin or other supplement consumption,dairy (full fat or reduced fat), coffee/tea consumption, folic acid,sugar (complex, artificial, simple), processed food versus home cooked.Exposure to plastics: microwave in plastic, cook with plastic, storefood in plastic, plastic water or coffee mugs. Water consumption: amountper day, format: straight from the tap, bottled water (plastic orbottle), filtered (type: e.g. Britta/Pur) Residence history startingwith mother's pregnancy: location/duration Environmental exposure topotential toxins for different regions (extracted from governmentmonitoring databases) Health metrics: autoimmune disease, chronicillness/condition Pelvic surgery history Life time number of pelvicX-rays History of sexually transmitted infections:type/treatment/outcome Reproductive hormone levels: follicle stimulatinghormone, anti-Müllerian hormone, estrogen, progesterone Stress Thicknessand type of endometrium throughout the menstrual cycle. Age HeightFertility treatment history and details: history of hormone stimulation,brand of drugs used, basal antral follicle count, follicle count afterstimulation with different protocols, number/quality/stage of retrievedoocytes/development profile of embryos resulting from in vitroinsemination (natural or ICSI), details of IVF procedure (which clinic,doctor/embryologist at clinic, assisted hatching, fresh or thawedoocytes/embryos, embryo transfer (blood on the catheter/squirt detectionand direction on ultrasound), number of successful and unsuccessful IVFattempts Morning sickness during pregnancy Breast sizebefore/during/after pregnancy History of ovarian cysts Twin or siblingfrom multiple birth (mono-zygotic or di-zygotic) Male factor infertilityfor reproductive partner: Semen analysis (count, motility, morphology),Vasectomy, male cancer, smoking, alcohol, diet, STIs Blood type DESexposure in utero Past and current exercise/athletic history Levels ofphthalates, including metabolites: MEP—monoethyl phthalate,MECPP—mono(2-ethyl-5-carboxypentyl) phthalate,MEHHP—mono(2-ethyl-5-hydroxyhexyl) phthalate, MEOHP—mono(2-ethyl-5-ox-ohexyl) phthalate, MBP—monobutyl phthalate, MBzP—monobenzyl phthalate,MEHP—mono(2-ethylhexyl) phthalate, MiBP—mono-isobutyl phthalate,MCPP—mono(3-carboxypropyl) phthalate, MCOP—monocarboxyisooctylphthalate, MCNP—monocarboxyisononyl phthalate Familial history ofPremature Ovarian Failure/Insufficiency Autoimmunity history—Antiadrenalantibodies (anti-21-hydroxylase antibodies), antiovarian antibodies,antithyroid anitibodies (anti-thyroid peroxidase, antithyroglobulin)Hormone levels: Leutenizing hormone (using immunofluorometric assay),Δ4- Androstenedione (using radioimmunoassay), Dehydroepiandrosterone(using radioimmunoassay), and Inhibin B (commercial ELISA) Number ofyears trying to conceive Dioxin and PVC exposure Hair color Nevi (moles)Lead, cadmium, and other heavy metal exposure For a particular ARTcycle: the percentage of eggs that were abnormally fertilized, ifassisted hatching was performed, if anesthesia was used, average numberof cells contained by the embryo at the time of cryopreservation,average degree of expansion for blastocyst represented as a score,average degree of expansion of a previously frozen embryo represented asa score, embryo quality metrics including but not limited to degree ofcell fragmentation and visualization of a or organization/number ofcells contained in the inner cell mass (ICM), the fraction of overallembryos that make it to the blastocyst stage of development, the numberof embryos that make it to the blastocyst stage of development, use ofbirth control, the brand name of the hormones used in ovulationinduction, hyperstimulation syndrome, reason for cancelation of atreatment cycle, chemical pregnancy detected, clinical pregnancydetected, count of germinal vesicle containing oocytes upon retrieval,count of metaphase I stage eggs upon retrieval, count of metaphase IIstage eggs upon retrieval, count of embryos or oocytes arrested indevelopment and the stage of development or day of development postoocyte retrieval, number of embryos transferred and date in dayspost-oocyte retrieval that the embryos were transferred, how manyembryos were cryopreserved and at what stage of development

Information regarding the fertility-associated phenotypic traits of thefemale, such as those listed in Table 1, can be obtained by any meansknown in the art. In many cases, such information can be obtained from aquestionnaire completed by the subject that contains questions regardingcertain fertility-associated phenotypic traits. Additional informationcan be obtained from a questionnaire completed by the subject's partnerand blood relatives. The questionnaire includes questions regarding thesubject's fertility-associated phenotypic traits, such as her age,smoking habits, or frequency of alcohol consumption. Information canalso be obtained from the medical history of the subject, as well as themedical history of blood relatives and other family members. Additionalinformation can be obtained from the medical history and family medicalhistory of the subject's partner. Medical history information can beobtained through analysis of electronic medical records, paper medicalrecords, a series of questions about medical history included in thequestionnaire, and a combination thereof.

Clinical Samples

In other embodiments, information useful for determining the likelihoodof pregnancy is obtained by analyzing a sample collected from the femalesubject, reproductive partners of the subject, blood relatives of thesubject, gamete or embryo donors involved in the pregnancy effort,pregnancy surrogates, and a combination thereof. The sample may includea human tissue or bodily fluid and may be collected in any clinicallyacceptable manner. A tissue is a mass of connected cells and/orextracellular matrix material, e.g. skin tissue, hair, nails, nasalpassage tissue, CNS tissue, neural tissue, eye tissue, liver tissue,kidney tissue, placental tissue, mammary gland tissue, placental tissue,mammary gland tissue, gastrointestinal tissue, musculoskeletal tissue,genitourinary tissue, bone marrow, and the like, derived from, forexample, a human or other mammal and includes the connecting materialand the liquid material in association with the cells and/or tissues. Abody fluid is a liquid material derived from, for example, a human orother mammal. Such body fluids include, but are not limited to, mucous,blood, plasma, serum, serum derivatives, bile, blood, maternal blood,phlegm, saliva, sweat, amniotic fluid, menstrual fluid, mammary fluid,follicular fluid of the ovary, fallopian tube fluid, peritoneal fluid,urine, and cerebrospinal fluid (CSF), such as lumbar or ventricular CSF.A sample may also be a fine needle aspirate or biopsied tissue, e.g. anendometrial aspirate, breast tissue biopsy, and the like. A sample alsomay be media containing cells or biological material. A sample may alsobe a blood clot, for example, a blood clot that has been obtained fromwhole blood after the serum has been removed. In certain embodiments,the sample may include reproductive cells or tissues, such as gameticcells, gonadal tissue, fertilized embryos, and placenta. In certainembodiments, the sample is blood or saliva collected from the femalesubject.

In other embodiments, an assay specific to an environmental exposure isused to obtain the phenotypic trait of interest. Such assays are knownto those of skill in the art, and may be used with methods of theinvention. For example, the hormones used in birth control pills(estrogen and progesterone) may be detected from a urine or blood test.Venners et al. (Hum. Reprod. 21(9): 2272-2280, 2006) reports assays fordetecting estrogen and progesterone in urine and blood samples. Venneralso reports assays for detecting the chemicals used in fertilitytreatments.

Similarly, illicit drug use may be detected from a tissue or body fluid,such as hair, urine sweat, or blood, and there are numerous commerciallyavailable assays (LabCorp) for conducting such tests. Standard drugtests look for ten different classes of drugs, and the test iscommercially known as a “10-panel urine screen”. The 10-panel urinescreen consists of the following: 1. Amphetamines (includingMethamphetamine) 2. Barbiturates 3. Benzodiazepines 4. Cannabinoids(THC) 5. Cocaine 6. Methadone 7. Methaqualone 8. Opiates (Codeine,Morphine, Heroin, Oxycodone, Vicodin, etc.) 9. Phencyclidine (PCP) 10.Propoxyphene. Use of alcohol can also be detected by such tests.

Numerous assays can be used to tests a patient's exposure to plastics(e.g., Bisphenol A (BPA)). BPA is most commonly found as a component ofpolycarbonates (about 74% of total BPA produced) and in the productionof epoxy resins (about 20%). As well as being found in a myriad ofproducts including plastic food and beverage contains (including babyand water bottles), BPA is also commonly found in various householdappliances, electronics, sports safety equipment, adhesives, cashregister receipts, medical devices, eyeglass lenses, water supply pipes,and many other products. Assays for testing blood, sweat, or urine forpresence of BPA are described, for example, in Genuis et al. (Journal ofEnvironmental and Public Health, Volume 2012, Article ID 185731, 10pages, 2012).

Genotypic information from the sample can be obtained by nucleic acidextraction from the sample. Methods for extracting nucleic acid from asample are known in the art. See for example, Maniatis, et al.,Molecular Cloning: A Laboratory Manual, Cold Spring Harbor, N.Y., pp.280-281, 1982, the contents of which are incorporated by referenceherein in their entirety. In certain embodiments, a sample is collectedfrom a subject followed by enrichment for genes or gene fragments ofinterest, for example by hybridization to a nucleotide array includingfertility-related genetic regions or genetic fragments of interest. Thesample may be enriched for genetic regions of interest (e.g.,infertility-associated genetic regions) using methods known in the art,such as hybrid capture. See for examples, Lapidus (U.S. Pat. No.7,666,593), the content of which is incorporated by reference herein inits entirety.

RNA may be isolated from eukaryotic cells by procedures that involvelysis of the cells and denaturation of the proteins contained therein.Tissue of interest includes gametic cells, gonadal tissue, endometrialtissue, fertilized embryos, and placenta. Fluids of interest includeblood, menstrual fluid, mammary fluid, follicular fluid of the ovary,peritoneal fluid, or culture medium. Additional steps may be employed toremove DNA. Cell lysis may be accomplished with a nonionic detergent,followed by microcentrifugation to remove the nuclei and hence the bulkof the cellular DNA. In one embodiment, RNA is extracted from cells ofthe various types of interest using guanidinium thiocyanate lysisfollowed by CsCl centrifugation to separate the RNA from DNA (Chirgwinet al., Biochemistry 18:5294-5299 (1979)). Poly(A)+RNA is selected byselection with oligo-dT cellulose (see Sambrook et al., MOLECULARCLONING—A LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold Spring HarborLaboratory, Cold Spring Harbor, N.Y. (1989). Alternatively, separationof RNA from DNA can be accomplished by organic extraction, for example,with hot phenol or phenol/chloroform/isoamyl alcohol. If desired, RNaseinhibitors may be added to the lysis buffer. Likewise, for certain celltypes, it may be desirable to add a protein denaturation/digestion stepto the protocol.

For many applications, it is desirable to preferentially enrich mRNAwith respect to other cellular RNAs, such as transfer RNA (tRNA) andribosomal RNA (rRNA). Most mRNAs contain a poly(A) tail at their 3′ end.This allows them to be enriched by affinity chromatography, for example,using oligo(dT) or poly(U) coupled to a solid support, such as celluloseor Sephadex™ (see Ausubel et al., CURRENT PROTOCOLS IN MOLECULARBIOLOGY, vol. 2, Current Protocols Publishing, New York (1994). Oncebound, poly(A)+mRNA is eluted from the affinity column using 2 mMEDTA/0.1% SDS.

Biomarkers

In certain aspects of the invention, genotypic data is obtained from thecollected sample. It is known that certain genetic regions areassociated with infertility. Variations in these genetic regions mayaffect pregnancy outcomes; therefore, it may be necessary to collectgenotype data from the female subject

A biomarker generally refers to a molecule that may act as an indicatorof a biological state. Biomarkers for use with methods of the inventionmay be any marker that is associated with infertility. Exemplarybiomarkers include genes (e.g. any region of DNA encoding a functionalproduct), genetic regions (e.g. regions including genes and intergenicregions with a particular focus on regions conserved throughoutevolution in placental mammals), and gene products (e.g., RNA andprotein). In certain embodiments, the biomarker is aninfertility-associated genetic region. An infertility-associated geneticregion is any DNA sequence in which variation is associated with achange in fertility. Examples of changes in fertility include, but arenot limited to, the following: a homozygous mutation of aninfertility-associated gene leads to a complete loss of fertility; ahomozygous mutation of an infertility-associated gene is incompletelypenetrant and leads to reduction in fertility that varies fromindividual to individual; a heterozygous mutation is completelyrecessive, having no effect on fertility; and the infertility-associatedgene is X-linked, such that a potential defect in fertility depends onwhether a non-functional allele of the gene is located on an inactive Xchromosome (Ban body) or on an expressed X chromosome.

In particular embodiments, the assessed infertility-associated geneticregion is a maternal effect gene. Maternal effects genes are genes thathave been found to encode key structures and functions in mammalianoocytes (Yurttas et al., Reproduction 139:809-823, 2010). Maternaleffect genes are described, for example in, Christians et al. (Mol CellBiol 17:778-88, 1997); Christians et al., Nature 407:693-694, 2000);Xiao et al. (EMBO J 18:5943-5952, 1999); Tong et al. (Endocrinology145:1427-1434, 2004); Tong et al. (Nat Genet 26:267-268, 2000); Tong etal. (Endocrinology, 140:3720-3726, 1999); Tong et al. (Hum Reprod17:903-911, 2002); Ohsugi et al. (Development 135:259-269, 2008);Borowczyk et al. (Proc Natl Acad Sci USA., 2009); and Wu (Hum Reprod24:415-424, 2009). Maternal effects genes are also described in U.S.Ser. No. 12/889,304. The content of each of these is incorporated byreference herein in its entirety.

In particular embodiments, the infertility-associated genetic region isa gene (including exons, introns, and 10 kb of DNA flanking either sideof said gene) selected from the genes shown in Table 1 below. In Table1, OMIM reference numbers are provided when available

TABLE 2 Human Infertility-Related Genes (OMIM #) ABCA1 (600046) ACTL6A(604958) ACTL8 ACVR1 (102576) ACVR1B (601300) ACVR1C (608981) ACVR2(102581) ACVR2A (102581) ACVR2B (602730) ACVRL1 (601284) ADA (608958)ADAMTS1 (605174) ADM (103275) ADM2 (608682) AFF2 (300806) AGT (106150)AHR (600253) AIRE (607358) AK2 (103020) AK7 AKR1C1 (600449) AKR1C2(600450) AKR1C3 (603966) AKR1C4 (600451) AKT1 (164730) ALDOA (103850)ALDOB (612724) ALDOC (103870) ALPL (171760) AMBP (176870) AMD1 (180980)AMH (600957) AMHR2 (600956) ANK3 (600465) ANXA1 (151690) APC (611731)APOA1 (107680) APOE (107741) AQP4 (600308) AR (313700) AREG (104640)ARF1 (103180) ARF3 (103190) ARF4 (601177) ARF5 (103188) ARFRP1 (604699)ARL1 (603425) ARL10 (612405) ARL11 (609351) ARL13A ARL13B (608922) ARL15ARL2 (601175) ARL3 (604695) ARL4A (604786) ARL4C (604787) ARL4D (600732)ARL5A (608960) ARL5B (608909) ARL5C ARL6 (608845) ARL8A ARL8B ARMC2ARNTL (602550) ASCL2 (601886) ATF7IP (613644) ATG7 (608760) ATM (607585)ATR (601215) ATXN2 (601517) AURKA (603072) AURKB (604970) AUTS2 (607270)BARD1 (601593) BAX (600040) BBS1 (209901) BBS10 (610148) BBS12 (610683)BBS2 (606151) BBS4 (600374) BBS5 (603650) BBS7 (607590) BBS9 (607968)BCL2 (151430) BCL2L1 (600039) BCL2L10 (606910) BDNF (113505) BECN1(604378) BHMT (602888) BLVRB (600941) BMP15 (300247) BMP2 (112261) BMP3(112263) BMP4 (112262) BMP5 (112265) BMP6 (112266) BMP7 (112267) BMPR1A(601299) BMPR1B (603248) BMPR2 (600799) BNC1 (601930) BOP1 (610596)BRCA1 (113705) BRCA2 (600185) BRIP1 (605882) BRSK1 (609235) BRWD1 BSG(109480) BTG4 (605673) BUB1 (602452) BUB1B (602860) C2orf86 (613580) C3(120700) C3orf56 C6orf221 (611687) CA1 (114800) CARD8 (609051) CARM1(603934) CASP1 (147678) CASP2 (600639) CASP5 (602665) CASP6 (601532)CASP8 (601763) CBS (613381) CBX1 (604511) CBX2 (602770) CBX5 (604478)CCDC101 (613374) CCDC28B (610162) CCL13 (601391) CCL14 (601392) CCL4(182284) CCL5 (187011) CCL8 (602283) CCND1 (168461) CCND2 (123833) CCND3(123834) CCNH (601953) CCS (603864) CD19 (107265) CD24 (600074) CD55(125240) CD81 (186845) CD9 (143030) CDC42 (116952) CDK4 (123829) CDK6(603368) CDK7 (601955) CDKN1B (600778) CDKN1C (600856) CDKN2A (600160)CDX2 (600297) CDX4 (300025) CEACAM20 CEBPA (116897) CEBPB (189965) CEBPD(116898) CEBPE (600749) CEBPG (138972) CEBPZ (612828) CELF1 (601074)CELF4 (612679) CENPB (117140) CENPF (600236) CENPI (300065) CEP290(610142) CFC1 (605194) CGA (118850) CGB (118860) CGB1 (608823) CGB2(608824) CGB5 (608825) CHD7 (608892) CHST2 (603798) CLDN3 (602910) COIL(600272) COL1A2 (120160) COL4A3BP (604677) COMT (116790) COPE (606942)COX2 (600262) CP (117700) CPEB1 (607342) CRHR1 (122561) CRYBB2 (123620)CSF1 (120420) CSF2 (138960) CSTF1 (600369) CSTF2 (600368) CTCF (604167)CTCFL (607022) CTF2P CTGF (121009) CTH (607657) CTNNB1 (116806) CUL1(603134) CX3CL1 (601880) CXCL10 (147310) CXCL9 (601704) CXorf67 CYP11A1(118485) CYP11B1 (610613) CYP11B2 (124080) CYP17A1 (609300) CYP19A1(107910) CYP1A1 (108330) CYP27B1 (609506) DAZ2 (400026) DAZL (601486)DCTPP1 DDIT3 (126337) DDX11 (601150) DDX20 (606168) DDX3X (300160) DDX43(606286) DEPDC7 (612294) DHFR (126060) DHFRL1 DIAPH2 (300108) DICER1(606241) DKK1 (605189) DLC1 (604258) DLGAP5 DMAP1 (605077) DMC1 (602721)DNAJB1 (604572) DNMT1 (126375) DNMT3B (602900) DPPA3 (608408) DPPA5(611111) DPYD (612779) DTNBP1 (607145) DYNLL1 (601562) ECHS1 (602292)EEF1A1 (130590) EEF1A2 (602959) EFNA1 (191164) EFNA2 (602756) EFNA3(601381) EFNA4 (601380) EFNA5 (601535) EFNB1 (300035) EFNB2 (600527)EFNB3 (602297) EGR1 (128990) EGR2 (129010) EGR3 (602419) EGR4 (128992)EHMT1 (607001) EHMT2 (604599) EIF2B2 (606454) EIF2B4 (606687) EIF2B5(603945) EIF2C2 (606229) EIF3C (603916) EIF3CL (603916) EPHA1 (179610)EPHA10 (611123) EPHA2 (176946) EPHA3 (179611) EPHA4 (602188) EPHA5(600004) EPHA6 (600066) EPHA7 (602190) EPHA8 (176945) EPHB1 (600600)EPHB2 (600997) EPHB3 (601839) EPHB4 (600011) EPHB6 (602757) ERCC1(126380) ERCC2 (126340) EREG (602061) ESR1 (133430) ESR2 (601663) ESR2(601663) ESRRB (602167) ETV5 (601600) EZH2 (601573) EZR (123900) FANCC(613899) FANCG (602956) FANCL (608111) FAR1 FAR2 FASLG (134638) FBN1(134797) FBN2 (612570) FBN3 (608529) FBRS (608601) FBRSL1 FBXO10(609092) FBXO11 (607871) FCRL3 (606510) FDXR (103270) FGF23 (605380)FGF8 (600483) FGFBP1 (607737) FGFBP3 FGFR1 (136350) FHL2 (602633) FIGLA(608697) FILIP1L (612993) FKBP4 (600611) FMN2 (606373) FMR1 (309550)FOLR1 (136430) FOLR2 (136425) FOXE1 (602617) FOXL2 (605597) FOXN1(600838) FOXO3 (602681) FOXP3 (300292) FRZB (605083) FSHB (136530) FSHR(136435) FST (136470) GALT (606999) GBP5 (611467) GCK (138079) GDF1(602880) GDF3 (606522) GDF9 (601918) GGT1 (612346) GJA1 (121014) GJA10(611924) GJA3 (121015) GJA4 (121012) GJA5 (121013) GJA8 (600897) GJB1(304040) GJB2 (121011) GJB3 (603324) GJB4 (605425) GJB6 (604418) GJB7(611921) GJC1 (608655) GJC2 (608803) GJC3 (611925) GJD2 (607058) GJD3(607425) GJD4 (611922) GNA13 (604406) GNB2 (139390) GNRH1 (152760) GNRH2(602352) GNRHR (138850) GPC3 (300037) GPRC5A (604138) GPRC5B (605948)GREM2 (608832) GRN (138945) GSPT1 (139259) GSTA1 (138359) H19 (103280)H1FOO (142709) HABP2 (603924) HADHA (600890) HAND2 (602407) HBA1(141800) HBA2 (141850) HBB (141900) HELLS (603946) HK3 (142570) HMOX1(141250) HNRNPK (600712) HOXA11 (142958) HPGD (601688) HS6ST1 (604846)HSD17B1 (109684) HSD17B12 (609574) HSD17B2 (109685) HSD17B4 (601860)HSD17B7 (606756) HSD3B1 (109715) HSF1 (140580) HSF2BP (604554) HSP90B1(191175) HSPG2 (142461) HTATIP2 (605628) ICAM1 (147840) ICAM2 (146630)ICAM3 (146631) IDH1 (147700) IFI30 (604664) IFITM1 (604456) IGF1(147440) IGF1R (147370) IGF2 (147470) IGF2BP1 (608288) IGF2BP2 (608289)IGF2BP3 (608259) IGF2BP3 (608259) IGF2R (147280) IGFALS (601489) IGFBP1(146730) IGFBP2 (146731) IGFBP3 (146732) IGFBP4 (146733) IGFBP5 (146734)IGFBP6 (146735) IGFBP7 (602867) IGFBPL1 (610413) IL10 (124092) IL11RA(600939) IL12A (161560) IL12B (161561) IL13 (147683) IL17A (603149)IL17B (604627) IL17C (604628) IL17D (607587) IL17F (606496) IL1A(147760) IL1B (147720) IL23A (605580) IL23R (607562) IL4 (147780) IL5(147850) IL5RA (147851) IL6 (147620) IL6ST (600694) IL8 (146930) ILK(602366) INHA (147380) INHBA (147290) INHBB (147390) IRF1 (147575) ISG15(147571) ITGA11 (604789) ITGA2 (192974) ITGA3 (605025) ITGA4 (192975)ITGA7 (600536) ITGA9 (603963) ITGAV (193210) ITGB1 (135630) JAG1(601920) JAG2 (602570) JARID2 (601594) JMY (604279) KAL1 (300836) KDM1A(609132) KDM1B (613081) KDM3A (611512) KDM4A (609764) KDM5A (180202)KDM5B (605393) KHDC1 (611688) KIAA0430 (614593) KIF2C (604538) KISS1(603286) KISS1R (604161) KITLG (184745) KL (604824) KLF4 (602253) KLF9(602902) KLHL7 (611119) LAMC1 (150290) LAMC2 (150292) LAMP1 (153330)LAMP2 (309060) LAMP3 (605883) LDB3 (605906) LEP (164160) LEPR (601007)LFNG (602576) LHB (152780) LHCGR (152790) LHX8 (604425) LIF (159540)LIFR (151443) LIMS1 (602567) LIMS2 (607908) LIMS3 LIMS3L LIN28 (611043)LIN28B (611044) LMNA (150330) LOC613037 LOXL4 (607318) LPP (600700)LYRM1 (614709) MAD1L1 (602686) MAD2L1 (601467) MAD2L1BP MAF (177075)MAP3K1 (600982) MAP3K2 (609487) MAPK1 (176948) MAPK3 (601795) MAPK8(601158) MAPK9 (602896) MB21D1 (613973) MBD1 (156535) MBD2 (603547) MBD3(603573) MBD4 (603574) MCL1 (159552) MCM8 (608187) MDK (162096) MDM2(164785) MDM4 (602704) MECP2 (300005) MED12 (300188) MERTK (604705)METTL3 (612472) MGAT1 (160995) MITF (156845) MKKS (604896) MKS1 (609883)MLH1 (120436) MLH3 (604395) MOS (190060) MPPED2 (600911) MRS2 MSH2(609309) MSH3 (600887) MSH4 (602105) MSH5 (603382) MSH6 (600678) MST1(142408) MSX1 (142983) MSX2 (123101) MTA2 (603947) MTHFD1 (172460) MTHFR(607093) MTO1 (614667) MTOR (601231) MTRR (602568) MUC4 (158372) MVP(605088) MX1 (147150) MYC (190080) NAB1 (600800) NAB2 (602381) NAT1(108345) NCAM1 (116930) NCOA2 (601993) NCOR1 (600849) NCOR2 (600848) NDP(300658) NFE2L3 (604135) NLRP1 (606636) NLRP10 (609662) NLRP11 (609664)NLRP12 (609648) NLRP13 (609660) NLRP14 (609665) NLRP2 (609364) NLRP3(606416) NLRP4 (609645) NLRP5 (609658) NLRP6 (609650) NLRP7 (609661)NLRP8 (609659) NLRP9 (609663) NNMT (600008) NOBOX (610934) NODAL(601265) NOG (602991) NOS3 (163729) NOTCH1 (190198) NOTCH2 (600275) NPM2(608073) NPR2 (108961) NR2C2 (601426) NR3C1 (138040) NR5A1 (184757)NR5A2 (604453) NRIP1 (602490) NRIP2 NRIP3 (613125) NTF4 (162662) NTRK1(191315) NTRK2 (600456) NUPR1 (614812) OAS1 (164350) OAT (613349) OFD1(300170) OOEP (611689) ORAI1 (610277) OTC (300461) PADI1 (607934) PADI2(607935) PADI3 (606755) PADI4 (605347) PADI6 (610363) PAEP (173310)PAIP1 (605184) PARP12 (612481) PCNA (176740) PCP4L1 PDE3A (123805) PDK1(602524) PGK1 (311800) PGR (607311) PGRMC1 (300435) PGRMC2 (607735) PIGA(311770) PIM1 (164960) PLA2G2A (172411) PLA2G4C (603602) PLA2G7 (601690)PLAC1L PLAG1 (603026) PLAGL1 (603044) PLCB1 (607120) PMS1 (600258) PMS2(600259) POF1B (300603) POLG (174763) POLR3A (614258) POMZP3 (600587)POU5F1 (164177) PPID (601753) PPP2CB (176916) PRDM1 (603423) PRDM9(609760) PRKCA (176960) PRKCB (176970) PRKCD (176977) PRKCDBP PRKCE(176975) PRKCG (176980) PRKCQ (600448) PRKRA (603424) PRLR (176761)PRMT1 (602950) PRMT10 (307150) PRMT2 (601961) PRMT3 (603190) PRMT5(604045) PRMT6 (608274) PRMT7 (610087) PRMT8 (610086) PROK1 (606233)PROK2 (607002) PROKR1 (607122) PROKR2 (607123) PSEN1 (104311) PSEN2(600759) PTGDR (604687) PTGER1 (176802) PTGER2 (176804) PTGER3 (176806)PTGER4 (601586) PTGES (605172) PTGES2 (608152) PTGES3 (607061) PTGFR(600563) PTGFRN (601204) PTGS1 (176805) PTGS2 (600262) PTN (162095) PTX3(602492) QDPR (612676) RAD17 (603139) RAX (601881) RBP4 (180250) RCOR1(607675) RCOR2 RCOR3 RDH11 (607849) REC8 (608193) REXO1 (609614) REXO2(607149) RFPL4A (612601) RGS2 (600861) RGS3 (602189) RSPO1 (609595)RTEL1 (608833) SAFB (602895) SAR1A (607691) SAR1B (607690) SCARB1(601040) SDC3 (186357) SELL (153240) SEPHS1 (600902) SEPHS2 (606218)SERPINA10 (605271) SFRP1 (604156) SFRP2 (604157) SFRP4 (606570) SFRP5(604158) SGK1 (602958) SGOL2 (612425) SH2B1 (608937) SH2B2 (605300)SH2B3 (605093) SIRT1 (604479) SIRT2 (604480) SIRT3 (604481) SIRT4(604482) SIRT5 (604483) SIRT6 (606211) SIRT7 (606212) SLC19A1 (600424)SLC28A1 (606207) SLC28A2 (606208) SLC28A3 (608269) SLC2A8 (605245)SLC6A2 (163970) SLC6A4 (182138) SLCO2A1 (601460) SLITRK4 (300562) SMAD1(601595) SMAD2 (601366) SMAD3 (603109) SMAD4 (600993) SMAD5 (603110)SMAD6 (602931) SMAD7 (602932) SMAD9 (603295) SMARCA4 (603254) SMARCA5(603375) SMC1A (300040) SMC1B (608685) SMC3 (606062) SMC4 (605575) SMPD1(607608) SOCS1 (603597) SOD1 (147450) SOD2 (147460) SOD3 (185490) SOX17(610928) SOX3 (313430) SPAG17 SPARC (182120) SPIN1 (609936) SPN (182160)SPO11 (605114) SPP1 (166490) SPSB2 (611658) SPTB (182870) SPTBN1(182790) SPTBN4 (606214) SRCAP (611421) SRD5A1 (184753) SRSF4 (601940)SRSF7 (600572) ST5 (140750) STAG3 (608489) STAR (600617) STARD10 STARD13(609866) STARD3 (607048) STARD3NL (611759) STARD4 (607049) STARD5(607050) STARD6 (607051) STARD7 STARD8 (300689) STARD9 (614642) STAT1(600555) STAT2 (600556) STAT3 (102582) STAT4 (600558) STAT5A (601511)STAT5B (604260) STAT6 (601512) STC1 (601185) STIM1 (605921) STK3(605030) SULT1E1 (600043) SUZ12 (606245) SYCE1 (611486) SYCE2 (611487)SYCP1 (602162) SYCP2 (604105) SYCP3 (604759) SYNE1 (608441) SYNE2(608442) TAC3 (162330) TACC3 (605303) TACR3 (162332) TAF10 (600475) TAF3(606576) TAF4 (601796) TAF4B (601689) TAF5 (601787) TAF5L TAF8 (609514)TAF9 (600822) TAP1 (170260) TBL1X (300196) TBXA2R (188070) TCL1A(186960) TCL1B (603769) TCL6 (604412) TCN2 (613441) TDGF1 (187395) TERC(602322) TERF1 (600951) TERT (187270) TEX12 (605791) TEX9 TF (190000)TFAP2C (601602) TFPI (152310) TFPI2 (600033) TG (188450) TGFB1 (190180)TGFB1I1 (602353) TGFBR3 (600742) THOC5 (612733) THSD7B TLE6 (612399)TM4SF1 (191155) TMEM67 (609884) TNF (191160) TNFAIP6 (600410) TNFSF13B(603969) TOP2A (126430) TOP2B (126431) TP53 (191170) TP53I3 (605171)TP63 (603273) TP73 (601990) TPMT (187680) TPRXL (611167) TPT1 (600763)TRIM32 (602290) TSC2 (191092) TSHB (188540) TSIX (300181) TTC8 (608132)TUBB4Q (158900) TUFM (602389) TYMS (188350) UBB (191339) UBC (191340)UBD (606050) UBE2D3 (602963) UBE3A (601623) UBL4A (312070) UBL4B(611127) UIMC1 (609433) UQCR11 (609711) UQCRC2 (191329) USP9X (300072)VDR (601769) VEGFA (192240) VEGFB (601398) VEGFC (601528) VHL (608537)VIM (193060) VKORC1 (608547) VKORC1L1 (608838) WAS (300392) WISP2(603399) WNT7A (601570) WNT7B (601967) WT1 (607102) XDH (607633) XIST(314670) YBX1 (154030) YBX2 (611447) ZAR1 (607520) ZFX (314980) ZNF22(194529) ZNF267 (604752) ZNF689 ZNF720 ZNF787 ZNF84 ZP1 (195000) ZP2(182888) ZP3 (182889) ZP4 (613514)

The molecular products of the genes in Table 1 are involved in differentaspects of oocyte and embryo physiology from transcription andchromosome remodeling to RNA processing and binding. Mutations in theseclasses of genes result in fertility difficulties for mammals containingthese mutations. Exemplary genes that affect fertility are furtherdescribed below.

Peptidylarginine Deiminase 6 (PADI6)

Padi6 was originally cloned from a 2D murine egg proteome gel based onits relative abundance, and Padi6 expression in mice appears to bealmost entirely limited to the oocyte and pre-implantation embryo(Yurttas et al., 2010). Padi6 is first expressed in primordial oocytefollicles and persists, at the protein level, throughoutpre-implantation development to the blastocyst stage (Wright et al., DevBiol, 256:73-88, 2003). Inactivation of Padi6 leads to femaleinfertility in mice, with the Padi6-null developmental arrest occurringat the two-cell stage (Yurttas et al., 2008).

Nucleoplasmin 2 (NPM2)

Nucleoplasmin is another maternal effect gene, and is thought to bephosphorylated during mouse oocyte maturation. NPM2 exhibits a phosphatesensitive increase in mass during oocyte maturation. Increasedphosphorylation is retained through the pronuclear stage of development.NPM2 then becomes dephosphorylated at the two-cell stage and remains inthis form throughout the rest of pre-implantation development. Further,its expression pattern appears to be restricted to oocytes and earlyembryos. Immunofluorescence analysis of NPM2 localization shows thatNPM2 primarily localizes to the nucleus in mouse oocytes and earlyembryos. In mice, maternally-derived NPM2 is required for femalefertility (Burns et al., 2003).

Brahma-Related Gene 1 (BRG1)

Mammalian SWI/SNF-related chromatin remodeling complexes regulatetranscription and are believed to be involved in zygotic genomeactivation (ZGA). Such complexes are composed of approximately ninesubunits, which can be variable depending on cell type and tissue. TheBRG1 catalytic subunit exhibits DNA-dependent APTase activity, and theenergy derived from ATP hydrolysis alters the conformation and positionof nucleosomes. Brgl is expressed in oocytes and has been shown to beessential in the mouse as null homozygotes do not progress beyond theblastocyst stage (Bultman et al., 2000).

Factor Located in Oocytes Permitting Embryonic Development (FLOPED/OOEP)

The subcortical maternal complex (SCMC) is a poorly characterized murineoocyte structure to which several maternal effect gene products localize(Li et al. Dev Cell 15:416-425, 2008). PADI6, MATER, FILIA, TLE6, andFLOPED have been shown to localize to this complex (Li et al. Dev Cell15:416-425, 2008; Yurttas et al. Development 135:2627-2636, 2008). Thiscomplex is not present in the absence of Floped and Nlrp5, and similarto embryos resulting from Nlrp5-depleted oocytes, embryos resulting fromFloped-null oocytes do not progress past the two cell stage of mousedevelopment (Li et al., 2008). FLOPED is a small (19 kD) RNA bindingprotein that has also been characterized under the name of MOEP19 (Herret al., Dev Biol 314:300-316, 2008).

KH Domain Containing 3-Like, Subcortical Maternal Complex Member(FILIA/KHDC3L)

FILIA is another small RNA-binding domain containing maternallyinherited murine protein. FILIA was identified and named for itsinteraction with MATER (Ohsugi et al. Development 135:259-269, 2008).Like other components of the SCMC, maternal inheritance of the Khdc3gene product is required for early embryonic development. In mice, lossof Khdc3 results in a developmental arrest of varying severity with ahigh incidence of aneuploidy due, in part, to improper chromosomealignment during early cleavage divisions (Li et al., 2008). Khdc3depletion also results in aneuploidy, due to spindle checkpoint assembly(SAC) inactivation, abnormal spindle assembly, and chromosomemisalignment (Zheng et al. Proc Natl Acad Sci USA 106:7473-7478, 2009).

Basonuclin (BNC1)

Basonuclin is a zinc finger transcription factor that has been studiedin mice. It is found expressed in keratinocytes and germ cells (male andfemale) and regulates rRNA (via polymerase I) and mRNA (via polymeraseII) synthesis (Iuchi and Green, 1999; Wang et al., 2006). Depending onthe amount by which expression is reduced in oocytes, embryos may notdevelop beyond the 8-cell stage. In Bsn1 depleted mice, a normal numberof oocytes are ovulated even though oocyte development is perturbed, butmany of these oocytes cannot go on to yield viable offspring (Ma et al.,2006).

Zygote Arrest 1 (ZAR1)

Zar1 is an oocyte-specific maternal effect gene that is known tofunction at the oocyte to embryo transition in mice. High levels of Zar1expression are observed in the cytoplasm of murine oocytes, andhomozygous-null females are infertile: growing oocytes from Zar1-nullfemales do not progress past the two-cell stage.

In certain embodiments, the gene is a gene that is expressed in anoocyte. Exemplary genes include CTCF, ZFP57, POU5F1, SEBOX, and HDAC1.

In other embodiments, the gene is a gene that is involved in DNA repairpathways, including but not limited to, MLH1, PMS1 and PMS2. In otherembodiments, the gene is BRCA1 or BRCA2.

In other embodiments, the biomarker is a gene product (e.g., RNA orprotein) of an infertility-associated gene. In particular embodiments,the gene product is a gene product of a maternal effect gene. In otherembodiments, the gene product is a product of a gene from Table 2. Incertain embodiments, the gene product is a product of a gene that isexpressed in an oocyte, such as a product of CTCF, ZFP57, POU5F1, SEBOX,and HDAC1. In other embodiments, the gene product is a product of a genethat is involved in DNA repair pathways, such as a product of MLH1,PMS1, or PMS2. In other embodiments, gene product is a product of BRCA1or BRCA2.

In other embodiments, the biomarker may be an epigenetic factor, such asmethylation patterns (e.g., hypermethylation of CpG islands), genomiclocalization or post-translational modification of histone proteins, orgeneral post-translational modification of proteins such as acetylation,ubiquitination, phosphorylation, or others.

Assays

Genotype data regarding the above genetic regions can be obtained, forexample, by conducting an assay that detects either a mutation in aninfertility-associated genetic region or abnormal expression of aninfertility-associated genetic region. The presence of certain mutationsin those genetic regions or abnormal expression levels of those geneticregions is indicative a fertility outcomes, i.e., whether a pregnancy orlive birth is achievable. Exemplary mutations include, but are notlimited to, a single nucleotide polymorphism, a deletion, an insertion,an inversion, a genetic rearrangement, a copy number variation, or acombination thereof.

In particular embodiments, the assay is conducted on genetic regionsfrom Table 2 or gene products of genes from Table 2. Detaileddescriptions of conventional methods, such as those employed to make anduse nucleic acid arrays, amplification primers, hybridization probes,and the like can be found in standard laboratory manuals such as: GenomeAnalysis: A Laboratory Manual Series (Vols. I-IV), Cold Spring HarborLaboratory Press; PCR Primer: A Laboratory Manual, Cold Spring HarborLaboratory Press; and Sambrook, J et al., (2001) Molecular Cloning: ALaboratory Manual, 2nd ed. (Vols. 1-3), Cold Spring Harbor LaboratoryPress. Custom nucleic acid arrays are commercially available from, e.g.,Affymetrix (Santa Clara, Calif.), Applied Biosystems (Foster City,Calif.), and Agilent Technologies (Santa Clara, Calif.).

Methods of detecting mutations in genetic regions are known in the art.In certain embodiments, a mutation in a single infertility-associatedgenetic region selected from Table 2 indicates infertility. In otherembodiments, the assay is conducted on more than one genetic region fromTable 2 (e.g., all of the genes from Table 2), and a mutation in atleast two of the genetic regions from Table 2 indicates infertility. Inother embodiments, a mutation in at least three of the genetic regionsfrom Table 2 indicates infertility; a mutation in at least four of thegenetic regions from Table 2 indicates infertility; a mutation in atleast five of the genetic regions from Table 2 indicates infertility; amutation in at least six of the genetic regions from Table 2 indicatesinfertility; a mutation in at least seven of the genetic regions fromTable 2 indicates infertility; a mutation in at least eight of thegenetic regions from Table 2 indicates infertility; a mutation in atleast nine of the genetic regions from Table 2 indicates infertility; amutation in at least 10 of the genetic regions from Table 2 indicatesinfertility; a mutation in at least 15 of the genetic regions from Table2 indicates infertility; or a mutation in all of the genetic regionsfrom Table 2 indicates infertility.

In certain embodiments, a known single nucleotide polymorphism at aparticular position can be detected by single base extension for aprimer that binds to the sample DNA adjacent to that position. See forexample Shuber et al. (U.S. Pat. No. 6,566,101), the content of which isincorporated by reference herein in its entirety. In other embodiments,a hybridization probe might be employed that overlaps the SNP ofinterest and selectively hybridizes to sample nucleic acids containing aparticular nucleotide at that position. See for example Shuber et al.(U.S. Pat. Nos. 6,214,558 and 6,300,077), the content of which isincorporated by reference herein in its entirety.

In particular embodiments, nucleic acids are sequenced in order todetect variants (i.e., mutations) in the nucleic acid compared towild-type and/or non-mutated forms of the sequence. The nucleic acid caninclude a plurality of nucleic acids derived from a plurality of geneticelements. Methods of detecting sequence variants are known in the art,and sequence variants can be detected by any sequencing method known inthe art e.g., ensemble sequencing or single molecule sequencing.

Sequencing may be by any method known in the art. DNA sequencingtechniques include classic dideoxy sequencing reactions (Sanger method)using labeled terminators or primers and gel separation in slab orcapillary, sequencing by synthesis using reversibly terminated labelednucleotides, pyrosequencing, 454 sequencing, allele specifichybridization to a library of labeled oligonucleotide probes, sequencingby synthesis using allele specific hybridization to a library of labeledclones that is followed by ligation, real time monitoring of theincorporation of labeled nucleotides during a polymerization step,polony sequencing, and SOLiD sequencing. Sequencing of separatedmolecules has more recently been demonstrated by sequential or singleextension reactions using polymerases or ligases as well as by single orsequential differential hybridizations with libraries of probes

One conventional method to perform sequencing is by chain terminationand gel separation, as described by Sanger et al., Proc Natl. Acad. Sci.USA, 74(12): 5463 67 (1977). Another conventional sequencing methodinvolves chemical degradation of nucleic acid fragments. See, Maxam etal., Proc. Natl. Acad. Sci., 74: 560 564 (1977). Finally, methods havebeen developed based upon sequencing by hybridization. See, e.g., Harriset al., (U.S. patent application number 2009/0156412). The content ofeach reference is incorporated by reference herein in its entirety.

A sequencing technique that can be used in the methods of the providedinvention includes, for example, Helicos True Single Molecule Sequencing(tSMS) (Harris T. D. et al. (2008) Science 320:106-109). In the tSMStechnique, a DNA sample is cleaved into strands of approximately 100 to200 nucleotides, and a polyA sequence is added to the 3′ end of each DNAstrand. Each strand is labeled by the addition of a fluorescentlylabeled adenosine nucleotide. The DNA strands are then hybridized to aflow cell, which contains millions of oligo-T capture sites that areimmobilized to the flow cell surface. The templates can be at a densityof about 100 million templates/cm². The flow cell is then loaded into aninstrument, e.g., HeliScope™ sequencer, and a laser illuminates thesurface of the flow cell, revealing the position of each template. A CCDcamera can map the position of the templates on the flow cell surface.The template fluorescent label is then cleaved and washed away. Thesequencing reaction begins by introducing a DNA polymerase and afluorescently labeled nucleotide. The oligo-T nucleic acid serves as aprimer. The polymerase incorporates the labeled nucleotides to theprimer in a template directed manner. The polymerase and unincorporatednucleotides are removed. The templates that have directed incorporationof the fluorescently labeled nucleotide are detected by imaging the flowcell surface. After imaging, a cleavage step removes the fluorescentlabel, and the process is repeated with other fluorescently labelednucleotides until the desired read length is achieved. Sequenceinformation is collected with each nucleotide addition step. Furtherdescription of tSMS is shown for example in Lapidus et al. (U.S. Pat.No. 7,169,560), Lapidus et al. (U.S. patent application number2009/0191565), Quake et al. (U.S. Pat. No. 6,818,395), Harris (U.S. Pat.No. 7,282,337), Quake et al. (U.S. patent application number2002/0164629), and Braslaysky, et al., PNAS (USA), 100: 3960-3964(2003), the contents of each of these references is incorporated byreference herein in its entirety.

Another example of a DNA sequencing technique that can be used in themethods of the provided invention is 454 sequencing (Roche) (Margulies,M et al. 2005, Nature, 437, 376-380). 454 sequencing involves two steps.In the first step, DNA is sheared into fragments of approximately300-800 base pairs, and the fragments are blunt ended. Oligonucleotideadaptors are then ligated to the ends of the fragments. The adaptorsserve as primers for amplification and sequencing of the fragments. Thefragments can be attached to DNA capture beads, e.g.,streptavidin-coated beads using, e.g., Adaptor B, which contains5′-biotin tag. The fragments attached to the beads are PCR amplifiedwithin droplets of an oil-water emulsion. The result is multiple copiesof clonally amplified DNA fragments on each bead. In the second step,the beads are captured in wells (pico-liter sized). Pyrosequencing isperformed on each DNA fragment in parallel. Addition of one or morenucleotides generates a light signal that is recorded by a CCD camera ina sequencing instrument. The signal strength is proportional to thenumber of nucleotides incorporated. Pyrosequencing makes use ofpyrophosphate (PPi) which is released upon nucleotide addition. PPi isconverted to ATP by ATP sulfurylase in the presence of adenosine 5′phosphosulfate. Luciferase uses ATP to convert luciferin tooxyluciferin, and this reaction generates light that is detected andanalyzed.

Another example of a DNA sequencing technique that can be used in themethods of the provided invention is SOLiD technology (AppliedBiosystems). In SOLiD sequencing, genomic DNA is sheared into fragments,and adaptors are attached to the 5′ and 3′ ends of the fragments togenerate a fragment library. Alternatively, internal adaptors can beintroduced by ligating adaptors to the 5′ and 3′ ends of the fragments,circularizing the fragments, digesting the circularized fragment togenerate an internal adaptor, and attaching adaptors to the 5′ and 3′ends of the resulting fragments to generate a mate-paired library. Next,clonal bead populations are prepared in microreactors containing beads,primers, template, and PCR components. Following PCR, the templates aredenatured and beads are enriched to separate the beads with extendedtemplates. Templates on the selected beads are subjected to a 3′modification that permits bonding to a glass slide. The sequence can bedetermined by sequential hybridization and ligation of partially randomoligonucleotides with a central determined base (or pair of bases) thatis identified by a specific fluorophore. After a color is recorded, theligated oligonucleotide is cleaved and removed and the process is thenrepeated.

Another example of a DNA sequencing technique that can be used in themethods of the provided invention is Ion Torrent sequencing (U.S. patentapplication numbers 2009/0026082, 2009/0127589, 2010/0035252,2010/0137143, 2010/0188073, 2010/0197507, 2010/0282617, 2010/0300559),2010/0300895, 2010/0301398, and 2010/0304982), the content of each ofwhich is incorporated by reference herein in its entirety. In IonTorrent sequencing, DNA is sheared into fragments of approximately300-800 base pairs, and the fragments are blunt ended. Oligonucleotideadaptors are then ligated to the ends of the fragments. The adaptorsserve as primers for amplification and sequencing of the fragments. Thefragments can be attached to a surface and is attached at a resolutionsuch that the fragments are individually resolvable. Addition of one ormore nucleotides releases a proton (H⁺), which signal detected andrecorded in a sequencing instrument. The signal strength is proportionalto the number of nucleotides incorporated.

Another example of a sequencing technology that can be used in themethods of the provided invention is Illumina sequencing. Illuminasequencing is based on the amplification of DNA on a solid surface usingfold-back PCR and anchored primers. Genomic DNA is fragmented, andadapters are added to the 5′ and 3′ ends of the fragments. DNA fragmentsthat are attached to the surface of flow cell channels are extended andbridge amplified. The fragments become double stranded, and the doublestranded molecules are denatured. Multiple cycles of the solid-phaseamplification followed by denaturation can create several millionclusters of approximately 1,000 copies of single-stranded DNA moleculesof the same template in each channel of the flow cell. Primers, DNApolymerase and four fluorophore-labeled, reversibly terminatingnucleotides are used to perform sequential sequencing. After nucleotideincorporation, a laser is used to excite the fluorophores, and an imageis captured and the identity of the first base is recorded. The 3′terminators and fluorophores from each incorporated base are removed andthe incorporation, detection and identification steps are repeated.

Another example of a sequencing technology that can be used in themethods of the provided invention includes the single molecule,real-time (SMRT) technology of Pacific Biosciences. In SMRT, each of thefour DNA bases is attached to one of four different fluorescent dyes.These dyes are phospholinked. A single DNA polymerase is immobilizedwith a single molecule of template single stranded DNA at the bottom ofa zero-mode waveguide (ZMW). A ZMW is a confinement structure whichenables observation of incorporation of a single nucleotide by DNApolymerase against the background of fluorescent nucleotides thatrapidly diffuse in an out of the ZMW (in microseconds). It takes severalmilliseconds to incorporate a nucleotide into a growing strand. Duringthis time, the fluorescent label is excited and produces a fluorescentsignal, and the fluorescent tag is cleaved off. Detection of thecorresponding fluorescence of the dye indicates which base wasincorporated. The process is repeated.

Another example of a sequencing technique that can be used in themethods of the provided invention is nanopore sequencing (Soni G V andMeller A. (2007) Clin Chem 53: 1996-2001). A nanopore is a small hole,of the order of 1 nanometer in diameter. Immersion of a nanopore in aconducting fluid and application of a potential across it results in aslight electrical current due to conduction of ions through thenanopore. The amount of current which flows is sensitive to the size ofthe nanopore. As a DNA molecule passes through a nanopore, eachnucleotide on the DNA molecule obstructs the nanopore to a differentdegree. Thus, the change in the current passing through the nanopore asthe DNA molecule passes through the nanopore represents a reading of theDNA sequence.

Another example of a sequencing technique that can be used in themethods of the provided invention involves using a chemical-sensitivefield effect transistor (chemFET) array to sequence DNA (for example, asdescribed in US Patent Application Publication No. 20090026082). In oneexample of the technique, DNA molecules can be placed into reactionchambers, and the template molecules can be hybridized to a sequencingprimer bound to a polymerase. Incorporation of one or more triphosphatesinto a new nucleic acid strand at the 3′ end of the sequencing primercan be detected by a change in current by a chemFET. An array can havemultiple chemFET sensors. In another example, single nucleic acids canbe attached to beads, and the nucleic acids can be amplified on thebead, and the individual beads can be transferred to individual reactionchambers on a chemFET array, with each chamber having a chemFET sensor,and the nucleic acids can be sequenced.

Another example of a sequencing technique that can be used in themethods of the provided invention involves using a electron microscope(Moudrianakis E. N. and Beer M. Proc Natl Acad Sci USA. 1965 March;53:564-71). In one example of the technique, individual DNA moleculesare labeled using metallic labels that are distinguishable using anelectron microscope. These molecules are then stretched on a flatsurface and imaged using an electron microscope to measure sequences.

If the nucleic acid from the sample is degraded or only a minimal amountof nucleic acid can be obtained from the sample, PCR can be performed onthe nucleic acid in order to obtain a sufficient amount of nucleic acidfor sequencing (See e.g., Mullis et al. U.S. Pat. No. 4,683,195, thecontents of which are incorporated by reference herein in its entirety).

Methods of detecting levels of gene products (e.g., RNA or protein) areknown in the art. Commonly used methods known in the art for thequantification of mRNA expression in a sample include northern blottingand in situ hybridization (Parker & Barnes, Methods in Molecular Biology106:247 283 (1999), the contents of which are incorporated by referenceherein in their entirety); RNAse protection assays (Hod, Biotechniques13:852 854 (1992), the contents of which are incorporated by referenceherein in their entirety); and PCR-based methods, such as reversetranscription polymerase chain reaction (RT-PCR) (Weis et al., Trends inGenetics 8:263 264 (1992), the contents of which are incorporated byreference herein in their entirety). Alternatively, antibodies may beemployed that can recognize specific duplexes, including RNA duplexes,DNA-RNA hybrid duplexes, or DNA-protein duplexes. Other methods known inthe art for measuring gene expression (e.g., RNA or protein amounts) areshown in Yeatman et al. (U.S. patent application number 2006/0195269),the content of which is hereby incorporated by reference in itsentirety.

A differentially or abnormally expressed gene refers to a gene whoseexpression is activated to a higher or lower level in a subjectsuffering from a disorder, such as infertility, relative to itsexpression in a normal or control subject. The terms also include geneswhose expression is activated to a higher or lower level at differentstages of the same disorder. It is also understood that a differentiallyexpressed gene may be either activated or inhibited at the nucleic acidlevel or protein level, or may be subject to alternative splicing toresult in a different polypeptide product. Such differences may beevidenced by a change in mRNA levels, surface expression, secretion orother partitioning of a polypeptide, for example.

Differential gene expression may include a comparison of expressionbetween two or more genes or their gene products, or a comparison of theratios of the expression between two or more genes or their geneproducts, or even a comparison of two differently processed products ofthe same gene, which differ between normal subjects and subjectssuffering from a disorder, such as infertility, or between variousstages of the same disorder. Differential expression includes bothquantitative, as well as qualitative, differences in the temporal orcellular expression pattern in a gene or its expression products.Differential gene expression (increases and decreases in expression) isbased upon percent or fold changes over expression in normal cells.Increases may be of 1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120,140, 160, 180, or 200% relative to expression levels in normal cells.Alternatively, fold increases may be of 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5,5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, or 10 fold over expressionlevels in normal cells. Decreases may be of 1, 5, 10, 20, 30, 40, 50,55, 60, 65, 70, 75, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 99 or 100%relative to expression levels in normal cells.

In certain embodiments, reverse transcriptase PCR (RT-PCR) is used tomeasure gene expression. RT-PCR is a quantitative method that can beused to compare mRNA levels in different sample populations tocharacterize patterns of gene expression, to discriminate betweenclosely related mRNAs, and to analyze RNA structure.

The first step is the isolation of mRNA from a target sample. Thestarting material is typically total RNA isolated from human tissues orfluids.

General methods for mRNA extraction are well known in the art and aredisclosed in standard textbooks of molecular biology, including Ausubelet al., Current Protocols of Molecular Biology, John Wiley and Sons(1997). Methods for RNA extraction from paraffin embedded tissues aredisclosed, for example, in Rupp and Locker, Lab Invest. 56:A67 (1987),and De Andres et al., BioTechniques 18:42044 (1995). The contents ofeach of these references are incorporated by reference herein in theirentirety. In particular, RNA isolation can be performed usingpurification kit, buffer set and protease from commercial manufacturers,such as Qiagen, according to the manufacturer's instructions. Forexample, total RNA from cells in culture can be isolated using QiagenRNeasy mini-columns. Other commercially available RNA isolation kitsinclude MASTERPURE Complete DNA and RNA Purification Kit (EPICENTRE,Madison, Wis.), and Paraffin Block RNA Isolation Kit (Ambion, Inc.).Total RNA from tissue samples can be isolated using RNA Stat-60(Tel-Test). RNA prepared from tumor can be isolated, for example, bycesium chloride density gradient centrifugation.

The first step in gene expression profiling by RT-PCR is the reversetranscription of the RNA template into cDNA, followed by its exponentialamplification in a PCR reaction. The two most commonly used reversetranscriptases are avilo myeloblastosis virus reverse transcriptase(AMV-RT) and Moloney murine leukemia virus reverse transcriptase(MMLV-RT). The reverse transcription step is typically primed usingspecific primers, random hexamers, or oligo-dT primers, depending on thecircumstances and the goal of expression profiling. For example,extracted RNA can be reverse-transcribed using a GeneAmp RNA PCR kit(Perkin Elmer, Calif., USA), following the manufacturer's instructions.The derived cDNA can then be used as a template in the subsequent PCRreaction.

Although the PCR step can use a variety of thermostable DNA-dependentDNA polymerases, it typically employs the Taq DNA polymerase, which hasa 5′-3′ nuclease activity but lacks a 3′-5′ proofreading endonucleaseactivity. Thus, TaqMan® PCR typically utilizes the 5′-nuclease activityof Taq polymerase to hydrolyze a hybridization probe bound to its targetamplicon, but any enzyme with equivalent 5′ nuclease activity can beused. Two oligonucleotide primers are used to generate an amplicontypical of a PCR reaction. A third oligonucleotide, or probe, isdesigned to detect nucleotide sequence located between the two PCRprimers. The probe is non-extendible by Taq DNA polymerase enzyme, andis labeled with a reporter fluorescent dye and a quencher fluorescentdye. Any laser-induced emission from the reporter dye is quenched by thequenching dye when the two dyes are located close together as they areon the probe. During the amplification reaction, the Taq DNA polymeraseenzyme cleaves the probe in a template-dependent manner. The resultantprobe fragments disassociate in solution, and signal from the releasedreporter dye is free from the quenching effect of the secondfluorophore. One molecule of reporter dye is liberated for each newmolecule synthesized, and detection of the unquenched reporter dyeprovides the basis for quantitative interpretation of the data.

TaqMan® RT-PCR can be performed using commercially available equipment,such as, for example, ABI PRISM 7700™ Sequence Detection System™(Perkin-Elmer-Applied Biosystems, Foster City, Calif., USA), orLightcycler (Roche Molecular Biochemicals, Mannheim, Germany). Incertain embodiments, the 5′ nuclease procedure is run on a real-timequantitative PCR device such as the ABI PRISM 7700™ Sequence DetectionSystem™. The system consists of a thermocycler, laser, charge-coupleddevice (CCD), camera and computer. The system amplifies samples in a96-well format on a thermocycler. During amplification, laser-inducedfluorescent signal is collected in real-time through fiber optics cablesfor all 96 wells, and detected at the CCD. The system includes softwarefor running the instrument and for analyzing the data.

5′-Nuclease assay data are initially expressed as Ct, or the thresholdcycle. As discussed above, fluorescence values are recorded during everycycle and represent the amount of product amplified to that point in theamplification reaction. The point when the fluorescent signal is firstrecorded as statistically significant is the threshold cycle (Ct).

To minimize errors and the effect of sample-to-sample variation, RT-PCRis usually performed using an internal standard. The ideal internalstandard is expressed at a constant level among different tissues, andis unaffected by the experimental treatment. RNAs most frequently usedto normalize patterns of gene expression are mRNAs for the housekeepinggenes glyceraldehyde-3-phosphate-dehydrogenase (GAPDH) and actin, beta(ACTB). For performing analysis on pre-implantation embryos and oocytes,conserved helix-loop-helix ubiquitous kinase (CHUK) is a gene that isused for normalization.

A more recent variation of the RT-PCR technique is the real timequantitative PCR, which measures PCR product accumulation through adual-labeled fluorigenic probe (i.e., TaqMan® probe). Real time PCR iscompatible both with quantitative competitive PCR, in which internalcompetitor for each target sequence is used for normalization, and withquantitative comparative PCR using a normalization gene contained withinthe sample, or a housekeeping gene for RT-PCR. For further details see,e.g. Held et al., Genome Research 6:986 994 (1996), the contents ofwhich are incorporated by reference herein in their entirety.

In another embodiment, a MassARRAY-based gene expression profilingmethod is used to measure gene expression. In the MassARRAY-based geneexpression profiling method, developed by Sequenom, Inc. (San Diego,Calif.) following the isolation of RNA and reverse transcription, theobtained cDNA is spiked with a synthetic DNA molecule (competitor),which matches the targeted cDNA region in all positions, except a singlebase, and serves as an internal standard. The cDNA/competitor mixture isPCR amplified and is subjected to a post-PCR shrimp alkaline phosphatase(SAP) enzyme treatment, which results in the dephosphorylation of theremaining nucleotides. After inactivation of the alkaline phosphatase,the PCR products from the competitor and cDNA are subjected to primerextension, which generates distinct mass signals for the competitor- andcDNA-derives PCR products. After purification, these products aredispensed on a chip array, which is pre-loaded with components neededfor analysis with matrix-assisted laser desorption ionizationtime-of-flight mass spectrometry (MALDI-TOF MS) analysis. The cDNApresent in the reaction is then quantified by analyzing the ratios ofthe peak areas in the mass spectrum generated. For further details see,e.g. Ding and Cantor, Proc. Natl. Acad. Sci. USA 100:3059 3064 (2003).

Further PCR-based techniques include, for example, differential display(Liang and Pardee, Science 257:967 971 (1992)); amplified fragmentlength polymorphism (iAFLP) (Kawamoto et al., Genome Res. 12:1305 1312(1999)); BeadArray™ technology (Illumina, San Diego, Calif.; Oliphant etal., Discovery of Markers for Disease (Supplement to Biotechniques),June 2002; Ferguson et al., Analytical Chemistry 72:5618 (2000));BeadsArray for Detection of Gene Expression (BADGE), using thecommercially available Luminex100 LabMAP system and multiple color-codedmicrospheres (Luminex Corp., Austin, Tex.) in a rapid assay for geneexpression (Yang et al., Genome Res. 11:1888 1898 (2001)); and highcoverage expression profiling (HiCEP) analysis (Fukumura et al., Nucl.Acids. Res. 31(16) e94 (2003)). The contents of each of which areincorporated by reference herein in their entirety.

In certain embodiments, differential gene expression can also beidentified, or confirmed using a microarray technique. In this method,polynucleotide sequences of interest (including cDNAs andoligonucleotides) are plated, or arrayed, on a microchip substrate. Thearrayed sequences are then hybridized with specific DNA probes fromcells or tissues of interest. Methods for making microarrays anddetermining gene product expression (e.g., RNA or protein) are shown inYeatman et al. (U.S. patent application number 2006/0195269), thecontent of which is incorporated by reference herein in its entirety.

In a specific embodiment of the microarray technique, PCR amplifiedinserts of cDNA clones are applied to a substrate in a dense array, forexample, at least 10,000 nucleotide sequences are applied to thesubstrate. The microarrayed genes, immobilized on the microchip at10,000 elements each, are suitable for hybridization under stringentconditions. Fluorescently labeled cDNA probes may be generated throughincorporation of fluorescent nucleotides by reverse transcription of RNAextracted from tissues of interest. Labeled cDNA probes applied to thechip hybridize with specificity to each spot of DNA on the array. Afterstringent washing to remove non-specifically bound probes, the chip isscanned by confocal laser microscopy or by another detection method,such as a CCD camera. Quantitation of hybridization of each arrayedelement allows for assessment of corresponding mRNA abundance. With dualcolor fluorescence, separately labeled cDNA probes generated from twosources of RNA are hybridized pair-wise to the array. The relativeabundance of the transcripts from the two sources corresponding to eachspecified gene is thus determined simultaneously. The miniaturized scaleof the hybridization affords a convenient and rapid evaluation of theexpression pattern for large numbers of genes. Such methods have beenshown to have the sensitivity required to detect rare transcripts, whichare expressed at a few copies per cell, and to reproducibly detect atleast approximately two-fold differences in the expression levels(Schena et al., Proc. Natl. Acad. Sci. USA 93(2):106 149 (1996), thecontents of which are incorporated by reference herein in theirentirety). Microarray analysis can be performed by commerciallyavailable equipment, following manufacturer's protocols, such as byusing the Affymetrix GenChip technology, or Incyte's microarraytechnology.

Alternatively, protein levels can be determined by constructing anantibody microarray in which binding sites comprise immobilized,preferably monoclonal, antibodies specific to a plurality of proteinspecies encoded by the cell genome. Preferably, antibodies are presentfor a substantial fraction of the proteins of interest. Methods formaking monoclonal antibodies are well known (see, e.g., Harlow and Lane,1988, ANTIBODIES: A LABORATORY MANUAL, Cold Spring Harbor, N.Y., whichis incorporated in its entirety for all purposes). In one embodiment,monoclonal antibodies are raised against synthetic peptide fragmentsdesigned based on genomic sequence of the cell. With such an antibodyarray, proteins from the cell are contacted to the array, and theirbinding is assayed with assays known in the art. Generally, theexpression, and the level of expression, of proteins of diagnostic orprognostic interest can be detected through immunohistochemical stainingof tissue slices or sections.

Finally, levels of transcripts of marker genes in a number of tissuespecimens may be characterized using a “tissue array” (Kononen et al.,Nat. Med 4(7):844-7 (1998)). In a tissue array, multiple tissue samplesare assessed on the same microarray. The arrays allow in situ detectionof RNA and protein levels; consecutive sections allow the analysis ofmultiple samples simultaneously.

In other embodiments, Serial Analysis of Gene Expression (SAGE) is usedto measure gene expression. Serial analysis of gene expression (SAGE) isa method that allows the simultaneous and quantitative analysis of alarge number of gene transcripts, without the need of providing anindividual hybridization probe for each transcript. First, a shortsequence tag (about 10-14 bp) is generated that contains sufficientinformation to uniquely identify a transcript, provided that the tag isobtained from a unique position within each transcript. Then, manytranscripts are linked together to form long serial molecules, that canbe sequenced, revealing the identity of the multiple tagssimultaneously. The expression pattern of any population of transcriptscan be quantitatively evaluated by determining the abundance ofindividual tags, and identifying the gene corresponding to each tag. Formore details see, e.g. Velculescu et al., Science 270:484 487 (1995);and Velculescu et al., Cell 88:243 51 (1997, the contents of each ofwhich are incorporated by reference herein in their entirety).

In other embodiments Massively Parallel Signature Sequencing (MPSS) isused to measure gene expression. This method, described by Brenner etal., Nature Biotechnology 18:630 634 (2000), is a sequencing approachthat combines non-gel-based signature sequencing with in vitro cloningof millions of templates on separate 5 μm diameter microbeads. First, amicrobead library of DNA templates is constructed by in vitro cloning.This is followed by the assembly of a planar array of thetemplate-containing microbeads in a flow cell at a high density(typically greater than 3×106 microbeads/cm²). The free ends of thecloned templates on each microbead are analyzed simultaneously, using afluorescence-based signature sequencing method that does not require DNAfragment separation. This method has been shown to simultaneously andaccurately provide, in a single operation, hundreds of thousands of genesignature sequences from a yeast cDNA library.

Immunohistochemistry methods are also suitable for detecting theexpression levels of the gene products of the present invention. Thus,antibodies (monoclonal or polyclonal) or antisera, such as polyclonalantisera, specific for each marker are used to detect expression. Theantibodies can be detected by direct labeling of the antibodiesthemselves, for example, with radioactive labels, fluorescent labels,hapten labels such as, biotin, or an enzyme such as horse radishperoxidase or alkaline phosphatase. Alternatively, unlabeled primaryantibody is used in conjunction with a labeled secondary antibody,comprising antisera, polyclonal antisera or a monoclonal antibodyspecific for the primary antibody. Immunohistochemistry protocols andkits are well known in the art and are commercially available.

In certain embodiments, a proteomics approach is used to measure geneexpression. A proteome refers to the totality of the proteins present ina sample (e.g. tissue, organism, or cell culture) at a certain point oftime. Proteomics includes, among other things, study of the globalchanges of protein expression in a sample (also referred to asexpression proteomics). Proteomics typically includes the followingsteps: (1) separation of individual proteins in a sample by 2-D gelelectrophoresis (2-D PAGE); (2) identification of the individualproteins recovered from the gel, e.g. my mass spectrometry or N-terminalsequencing, and (3) analysis of the data using bioinformatics.Proteomics methods are valuable supplements to other methods of geneexpression profiling, and can be used, alone or in combination withother methods, to detect the products of the prognostic markers of thepresent invention.

In some embodiments, mass spectrometry (MS) analysis can be used aloneor in combination with other methods (e.g., immunoassays or RNAmeasuring assays) to determine the presence and/or quantity of the oneor more biomarkers disclosed herein in a biological sample. In someembodiments, the MS analysis includes matrix-assisted laserdesorption/ionization (MALDI) time-of-flight (TOF) MS analysis, such asfor example direct-spot MALDI-TOF or liquid chromatography MALDI-TOFmass spectrometry analysis. In some embodiments, the MS analysiscomprises electrospray ionization (ESI) MS, such as for example liquidchromatography (LC) ESI-MS. Mass analysis can be accomplished usingcommercially-available spectrometers. Methods for utilizing MS analysis,including MALDI-TOF MS and ESI-MS, to detect the presence and quantityof biomarker peptides in biological samples are known in the art. See,for example, U.S. Pat. Nos. 6,925,389; 6,989,100; and 6,890,763, each ofwhich is incorporated by reference herein in their entirety.

Prognosis Predictor

The information collected from the female subject is then compared to areference set of data in order to provide a probability of achievingpregnancy. In certain aspects, the reference set includes data collectedfrom of a cohort or plurality of women that have previously undergonethe selected fertility treatment. Such data may include thefertility-associated phenotypic traits of the women,fertility-associated medical interventions, and their pregnancy outcome,i.e., whether or not a pregnancy or live-birth was achieved, per cycleof the selected reproductive method. For example, information collectedfrom the women from the reference set could include age, smoking habits,alcohol intake, etc. The reference set could also include informationregarding the fertility-associated traits of the women from thereference set. Information can be obtained by any means known in theart. In certain embodiments, the information is obtained via aquestionnaire. In other embodiments, information can be obtained byanalyzing a sample collected from the women in the reference set. Infurther embodiments of the invention, when data comprising thefertility-associated phenotypic traits of a male subject is obtained,the reference set will include data regarding those traits collectedfrom a plurality of men. Additional details for preparing a mass dataset for use, for example, in IVF studies are provided in Malizia et al.,Cumulative live-birth rates after in vitro fertilization, N Engl J Med2009; 360: 236-43, incorporated by reference herein in its entirety.

The invention provides methods and systems for predicting a pregnancyoutcome in a female subject based on the subject's fertility-associatedphenotypic traits and/or genotypic data. In some embodiments, methodsand systems of the invention use a prognosis predictor for predictingpregnancy outcomes. The prognosis predictor can be based on anyappropriate pattern recognition method that receives input datarepresentative of a plurality of fertility-associated phenotypic traitsand provides an output that indicates a probability of achievingpregnancy or a live birth. The prognosis predictor is trained withtraining data from a plurality of women for whom fertility-associatedphenotypic traits, fertility-associated medical interventions, andpregnancy outcomes are known. The plurality of women used to train theprognosis predictor is also known as the training population. For eachwoman in the training population, the training data comprises (a) datarepresentative of a plurality of fertility-associated phenotypic traits;(b) fertility-associated medical interventions; and (c) pregnancyoutcome information (i.e., whether or not pregnancy occurred over apredetermined time period, for example, at a given cycle of IVF).Various prognosis predictors that can be used in conjunction with thepresent invention are described below. In some embodiments, additionalwomen having known trait profiles and pregnancy outcomes can be used totest the accuracy of the prognosis predictor obtained using the trainingpopulation. Such additional patients are known as the testingpopulation.

In certain embodiments, the methods of invention use a prognosispredictor, also called a classifier, for determining the probability ofachieving pregnancy. As noted above, the prognosis predictor can bebased on any appropriate pattern recognition method that receives aprofile, such as a profile based on a plurality of fertility-associatedphenotypic traits and provides an output comprising data indicating agood prognosis or a poor prognosis, i.e., whether or not pregnancy orlive birth will be achieved. As discussed previously, the profile can beobtained by completion of a questionnaire containing questions regardingcertain fertility-associated phenotypic traits or the collection of abiological sample to obtain genotypic data or a combination thereof. Theprognosis predictor is trained with training data from a trainingpopulation of women for whom fertility-associated phenotypic traits,fertility-associated medical interventions, and pregnancy outcomes areknown.

A prognosis predictor based on any of such methods can be constructedusing the profiles and prognosis data of the training patients. Such aprognosis predictor can then be used to predict the pregnancy outcome ofa female subject based on her profile of fertility-associated phenotypictraits, genotypic traits, or both. The methods can also be used toidentify traits that discriminate between achieving pregnancy and notachieving pregnancy using a trait profile and prognosis data of thetraining population.

In one embodiment, the prognosis predictor can be prepared by (a)generating a reference set of women for whom fertility-associatedphenotypic traits, fertility-associated medical interventions, andpregnancy outcomes are known; (b) determining for each trait, a metricof correlation between the trait and pregnancy outcome in a plurality ofwomen having known pregnancy outcomes at a predetermined time; (c)selecting one or more traits based on said level of correlation; (d)training a prognosis predictor, in which the prognosis predictorreceives data representative of the traits selected in the prior stepand provides an output indicating a probability of achieving pregnancy,with training data from the reference set of subjects includingassessments of traits taken from the women.

Various known statistical pattern recognition methods can be used inconjunction with the present invention. Suitable statistical methodsinclude, without limitation, logic regression, ordinal logisticregression, linear or quadratic discriminant analysis, clustering,principal component analysis, nearest neighbor classifier analysis, andCox proportional hazards regression. Non-limiting examples ofimplementing particular prognosis predictors in conjunction are providedherein to demonstrate the implementation of statistical methods inconjunction with the training set.

In some embodiments, the prognosis predictor is based on a regressionmodel, preferably a logistic regression model. Such a regression modelincludes a coefficient for each of the markers in a selected set ofmarkers of the invention. In such embodiments, the coefficients for theregression model are computed using, for example, a maximum likelihoodapproach.

Cox proportional hazards regression also includes a coefficient for eachof the markers in a selected set of markers of the invention. Coxproportional hazards regression incorporates censored data (women in thereference set that did not return for treatment). In such embodiments,the coefficients for the regression model are computed using, forexample, a maximum partial likelihood approach.

Some embodiments of the present invention provide generalizations of thelogistic regression model that handle multicategory (polychotomous)responses. Such embodiments can be used to discriminate an organism intoone or three or more prognosis groups. Such regression models usemulticategory logit models that simultaneously refer to all pairs ofcategories, and describe the odds of response in one category instead ofanother. Once the model specifies logits for a certain (J-1) pairs ofcategories, the rest are redundant. See, for example, Agresti, AnIntroduction to Categorical Data Analysis, John Wiley & Sons, Inc.,1996, New York, Chapter 8, which is hereby incorporated by reference.Linear discriminant analysis (LDA) attempts to classify a subject intoone of two categories based on certain object properties. In otherwords, LDA tests whether object attributes measured in an experimentpredict categorization of the objects. LDA typically requires continuousindependent variables and a dichotomous categorical dependent variable.In the present invention, the selected fertility-associated phenotypictraits serve as the requisite continuous independent variables. Theprognosis group classification of each of the members of the trainingpopulation serves as the dichotomous categorical dependent variable.

LDA seeks the linear combination of variables that maximizes the ratioof between-group variance and within-group variance by using thegrouping information. Implicitly, the linear weights used by LDA dependon how selected fertility-associated phenotypic trait manifests in thetwo groups (e.g., a group that achieves pregnancy and a group that doesnot) and how the selected trait correlates with the manifestation ofother traits. For example, LDA can be applied to the data matrix of theN members in the training sample by K genes in a combination of genesdescribed in the present invention. Then, the linear discriminant ofeach member of the training population is plotted. Ideally, thosemembers of the training population representing a first subgroup (e.g.those subjects that do not achieve pregnancy) will cluster into onerange of linear discriminant values (e.g., negative) and those member ofthe training population representing a second subgroup (e.g. thosesubjects that achieve pregnancy) will cluster into a second range oflinear discriminant values (e.g., positive). The LDA is considered moresuccessful when the separation between the clusters of discriminantvalues is larger. For more information on linear discriminant analysis,see Duda, Pattern Classification, Second Edition, 2001, John Wiley &Sons, Inc; and Hastie, 2001, The Elements of Statistical Learning,Springer, New York; Venables & Ripley, 1997, Modern Applied Statisticswith s-plus, Springer, New York.

Quadratic discriminant analysis (QDA) takes the same input parametersand returns the same results as LDA. QDA uses quadratic equations,rather than linear equations, to produce results. LDA and QDA areinterchangeable, and which to use is a matter of preference and/oravailability of software to support the analysis. Logistic regressiontakes the same input parameters and returns the same results as LDA andQDA.

In some embodiments of the present invention, decision trees are used toclassify patients using expression data for a selected set of molecularmarkers of the invention. Decision tree algorithms belong to the classof supervised learning algorithms. The aim of a decision tree is toinduce a classifier (a tree) from real-world example data. This tree canbe used to classify unseen examples which have not been used to derivethe decision tree.

A decision tree is derived from training data. An example containsvalues for the different attributes and what class the example belongs.In one embodiment, the training data is data representative of aplurality of fertility-associated phenotypic traits,fertility-associated medical interventions, and pregnancy outcomes.

The following algorithm describes a decision tree derivation:

Tree (Examples,Class,Attributes)

Create a root node

If all Examples have the same Class value, give the root this label

Else if Attributes is empty label the root according to the most

common value

Else begin

Calculate the information gain for each attribute

Select the attribute A with highest information gain and make

this the root attribute

For each possible value, v, of this attribute

Add a new branch below the root, corresponding to A=v

Let Examples(v) be those examples with A=v

If Examples(v) is empty, make the new branch a leaf node labeled

with the most common value among Examples

Else let the new branch be the tree created by

Tree (Examples(v),Class,Attributes-{A})

end

A more detailed description of the calculation of information gain isshown in the following. If the possible classes vi of the examples haveprobabilities P(vi) then the information content I of the actual answeris given by:I(P(v ₁), . . . ,P(v _(n)))=nΣi=1−P(v _(i))log₂ P(v _(i))

The I-value shows how much information we need in order to be able todescribe the outcome of a classification for the specific dataset used.Supposing that the dataset contains p positive (e.g. pregnancyachievers) and n negative (e.g. pregnancy non-achievers) examples (e.g.individuals), the information contained in a correct answer is:I(p/p+n,n/p+n)=−p/p+n log₂ p/p+n−n/p+n log₂ n/p+n

where log₂ is the logarithm using base two. By testing single attributesthe amount of information needed to make a correct classification can bereduced. The remainder for a specific attribute A (e.g. a trait) showshow much the information that is needed can be reduced.Remainder(A)=vΣi=1p _(i) +n _(i) /p+nI(p _(i) /pi+n _(i) ,n _(i) /p _(i)+n _(i))

“v” is the number of unique attribute values for attribute A in acertain dataset, “i” is a certain attribute value, “p,” is the number ofexamples for attribute A where the classification is positive (e.g.pregnancy achiever), “n,” is the number of examples for attribute Awhere the classification is negative (e.g., pregnancy non-achiever).

The information gain of a specific attribute A is calculated as thedifference between the information content for the classes and theremainder of attribute A:Gain(A)=I(p/p+n,n/p+n)−Remainder(A)

The information gain is used to evaluate how important the differentattributes are for the classification (how well they split up theexamples), and the attribute with the highest information.

In general there are a number of different decision tree algorithms,many of which are described in Duda, Pattern Classification, SecondEdition, 2001, John Wiley & Sons, Inc. Decision tree algorithms oftenrequire consideration of feature processing, impurity measure, stoppingcriterion, and pruning. Specific decision tree algorithms include, cutare not limited to classification and regression trees (CART),multivariate decision trees, ID3, and C4.5.

In one approach, when an exemplary embodiment of a decision tree isused, the data representative of a plurality of fertility-associatedphenotypic traits across a training population is standardized to havemean zero and unit variance. The members of the training population arerandomly divided into a training set and a test set. For example, in oneembodiment, two thirds of the members of the training population areplaced in the training set and one third of the members of the trainingpopulation are placed in the test set. The expression values for aselect combination of traits are used to construct the decision tree.Then, the ability for the decision tree to correctly classify members inthe test set is determined. In some embodiments, this computation isperformed several times for a given combination of molecular markers. Ineach iteration of the computation, the members of the trainingpopulation are randomly assigned to the training set and the test set.Then, the quality of the combination of traits is taken as the averageof each such iteration of the decision tree computation.

In some embodiments, the fertility-associated phenotypic traits and/orgenotypic data are used to cluster a training set. For example, considerthe case in which ten genes described in the present invention are used.Each member m of the training population will have expression values foreach of the ten genes. Such values from a member m in the trainingpopulation define the vector:

X_(1m) X_(2m) X_(3m) X_(4m) X_(5m) X_(6m) X_(7m) X_(8m) X_(9m) X_(10m)

where X_(im) is the expression level of the i^(th) gene in organism m.If there are m organisms in the training set, selection of i genes willdefine m vectors. Note that the methods of the present invention do notrequire that each the expression value of every single trait used in thevectors be represented in every single vector m. In other words, datafrom a subject in which one of the ith traits is not found can still beused for clustering. In such instances, the missing expression value isassigned either a “zero” or some other normalized value. In someembodiments, prior to clustering, the trait expression values arenormalized to have a mean value of zero and unit variance.

Those members of the training population that exhibit similar expressionpatterns across the training group will tend to cluster together. Aparticular combination of traits of the present invention is consideredto be a good classifier in this aspect of the invention when the vectorscluster into the trait groups found in the training population. Forinstance, if the training population includes patients with good or poorprognosis, a clustering classifier will cluster the population into twogroups, with each group uniquely representing either good or poorprognosis.

Clustering is described on pages 211-256 of Duda and Hart, PatternClassification and Scene Analysis, 1973, John Wiley & Sons, Inc., NewYork. As described in Section 6.7 of Duda, the clustering problem isdescribed as one of finding natural groupings in a dataset. To identifynatural groupings, two issues are addressed. First, a way to measuresimilarity (or dissimilarity) between two samples is determined. Thismetric (similarity measure) is used to ensure that the samples in onecluster are more like one another than they are to samples in otherclusters. Second, a mechanism for partitioning the data into clustersusing the similarity measure is determined.

Similarity measures are discussed in Section 6.7 of Duda, where it isstated that one way to begin a clustering investigation is to define adistance function and to compute the matrix of distances between allpairs of samples in a dataset. If distance is a good measure ofsimilarity, then the distance between samples in the same cluster willbe significantly less than the distance between samples in differentclusters. However, as stated on page 215 of Duda, clustering does notrequire the use of a distance metric. For example, a nonmetricsimilarity function s(x, x′) can be used to compare two vectors x andx′. Conventionally, s(x, x′) is a symmetric function whose value islarge when x and x′ are somehow “similar”. An example of a nonmetricsimilarity function s(x, x′) is provided on page 216 of Duda.

Once a method for measuring “similarity” or “dissimilarity” betweenpoints in a dataset has been selected, clustering requires a criterionfunction that measures the clustering quality of any partition of thedata. Partitions of the data set that extremize the criterion functionare used to cluster the data. See page 217 of Duda. Criterion functionsare discussed in Section 6.8 of Duda.

More recently, Duda et al., Pattern Classification, 2nd edition, JohnWiley & Sons, Inc. New York, has been published. Pages 537-563 describeclustering in detail. More information on clustering techniques can befound in Kaufman and Rousseeuw, 1990, Finding Groups in Data: AnIntroduction to Cluster Analysis, Wiley, New York, N.Y.; Everitt, 1993,Cluster analysis (3d ed.), Wiley, New York, N.Y.; and Backer, 1995,Computer-Assisted Reasoning in Cluster Analysis, Prentice Hall, UpperSaddle River, N.J. Particular exemplary clustering techniques that canbe used in the present invention include, but are not limited to,hierarchical clustering (agglomerative clustering using nearest-neighboralgorithm, farthest-neighbor algorithm, the average linkage algorithm,the centroid algorithm, or the sum-of-squares algorithm), k-meansclustering, fuzzy k-means clustering algorithm, and Jarvis-Patrickclustering.

Nearest neighbor classifiers are memory-based and require no model to befit. Given a query point x₀, the k training points x_((r)), r, . . . , kclosest in distance to x₀ are identified and then the point x₀ isclassified using the k nearest neighbors. Ties can be broken at random.In some embodiments, Euclidean distance in feature space is used todetermine distance as:d _((i)) =∥x _((i)) −x _(o)∥.

Typically, when the nearest neighbor algorithm is used, the expressiondata used to compute the linear discriminant is standardized to havemean zero and variance 1. In the present invention, the members of thetraining population are randomly divided into a training set and a testset. For example, in one embodiment, two thirds of the members of thetraining population are placed in the training set and one third of themembers of the training population are placed in the test set. Profilesrepresent the feature space into which members of the test set areplotted. Next, the ability of the training set to correctly characterizethe members of the test set is computed. In some embodiments, nearestneighbor computation is performed several times for a given combinationof fertility-associated phenotypic traits. In each iteration of thecomputation, the members of the training population are randomlyassigned to the training set and the test set. Then, the quality of thecombination of traits is taken as the average of each such iteration ofthe nearest neighbor computation.

The nearest neighbor rule can be refined to deal with issues of unequalclass priors, differential misclassification costs, and featureselection. Many of these refinements involve some form of weightedvoting for the neighbors. For more information on nearest neighboranalysis, see Duda, Pattern Classification, Second Edition, 2001, JohnWiley & Sons, Inc; and Hastie, 2001, The Elements of StatisticalLearning, Springer, New York.

The pattern classification and statistical techniques described aboveare merely examples of the types of models that can be used to constructa model for classification. It is to be understood that any statisticalmethod can be used in accordance with the invention. Moreover,combinations of these described above also can be used. Further detailon other statistical methods and their implementation are described inU.S. patent application Ser. No. 11/134,688, incorporated by referenceherein in its entirety

It is understood that during the course of treatments, women thatmake-up the reference set may drop out prior to achieving a pregnancy ora live birth. It is not known whether those women eventually achieve apregnancy at some later point or if they never became pregnant. Simplyomitting those women from the reference set would result bias to thereference data set by omitting characteristics of women having a poorprognosis of achieving a pregnancy or a live-birth. Such a bias wouldresult in reporting an overly optimistic probability of achieving apregnancy or live birth in connection with a particular fertilitytreatment.

With systems and methods of the invention, rather than omitting thosesubjects wholesale, the present invention takes advantage of certainmethods of statistical analysis to account for dropouts. TheKaplan-Meier method, for example, can be used to censor or exclude datafor women in the reference set that did not return for treatment. Otherforms of statistical analysis can be used in accordance with the presentinvention to compile the data of the reference set. For example,logistic regression, ordinal logistic regression, Cox proportionalhazards regression, and other methods can all be used to compile thedata within the reference set. In addition, it is contemplated that thereference set can censor or account for dropouts based on thefertility-associated traits of the women rather than making blanketassumptions regarding the fertility status of the dropouts. For example,rather than simply assuming that a dropout had the same chance ofbecoming pregnant as the women who continued treatment, or assuming thata dropout had no chance of becoming pregnant, the present invention canevaluate the fertility-associated traits of the dropouts andinformatively censor the dropouts based on such information. In thismanner, overly-optimistic estimates (resulting from the assumption thatall dropouts had equal chances of achieving live birth) oroverly-conservative estimates (resulting from the assumption that thedropouts had no chances of achieving live birth) are avoided.

In certain aspects, the present invention incorporates the use ofartificial censoring to account for dropouts. In artificial censoring,participants are censored when they meet a predefined study criterion,such as exposure to an intervention, noncompliance with their treatmentregimen, or the occurrence of a competing outcome. Further analyticalmethods, such as inverse-probability-of-censoring weights (IPCW), canthen be used to determine what the survival experiences of theartificially censored participants would have been had they never beenexposed to the intervention, complied, or not developed the competingoutcome. In some embodiments, methods encompassing the use of artificialcensoring and further, the use of IPCW are encompassed by the inventionto account for dropouts in the reference set. Additional detailregarding the use of artificial censoring and the use of IPCW isdescribed in Howe et al., Limitation of inverse probability-of-censoringweights in estimating survival in the presence of strong selection bias,Am J Epidemiology, 2011, incorporated by reference herein in itsentirety.

As mentioned above, the information collected from the female subject isrun through an algorithm trained on the reference set of data in orderto provide a probability of pregnancy for a selected cycle of treatment.The pregnancy outcomes per cycle of treatment for the matched traits arethen identified. Based on the identified pregnancy outcomes, theprobability of pregnancy for the female subject for a given cycle oftreatment is provided. Various statistical models, as discussed above,can be used in accordance with the invention to improve the accuracy ofthe determination.

In further aspects of the invention, the fertility-associated traitswithin the reference set that are assessed for determining theprobability of achieving a pregnancy are adjusted per cycle oftreatment. For example, in a first round of in vitro fertilization, awoman's drinking or smoking habits may be especially relevant. In alater round, however, a women's age may be more pertinent. Accordingly,aspects of the invention encompass adjusting the assessedfertility-associated traits per cycle of treatment. Methods of theinvention also include adjusting the assessed fertility-associatedtraits according to the selected fertility-associated medicalintervention. For example, if IVF is the selected procedure, thecondition of the woman's uterus may be more important than in ZIFT,which uses the Fallopian tubes rather than the uterus for implantation.

The advantages of the disclosed methods are depicted in FIGS. 2 and 3.FIG. 2 charts the cumulative probability of live birth versus cycles ofIVF treatment. The naïve constant chance/cycle and constant chance/cycleare conventional methods that have been used to predict a woman'schances of achieving pregnancy. The naïve constant chance/cycle methodassumes that a woman's odds of achieving live birth are exactly the samefor each cycle of IVF. Therefore, if a woman's probability of achievinglive birth at a first cycle of IVF is 25%, she will have a 50%cumulative probability at a second cycle, a 75% cumulative probabilityat a third cycle, and a 100% cumulative probability of achieving livebirth after four cycles of IVF, according to the naïve constant chancemethod.

The constant chance method still assumes each woman has a 25%probability of achieving live birth after a cycle of IVF but whendetermining the cumulative probability, applies the 25% probability tothe percentage of women still not pregnant. For example, the constantchance method assumes that for the first cycle, the probability ofachieving live birth is 25%. But for the second cycle, the 25%probability is applied to the 75% of the population still not pregnant,resulting in 19% probability of achieving live birth and a cumulativeprobability of 44% (25% first cycle+19% second cycle). For the thirdcycle, the cumulative probability is 58%. After four cycles of IVF, thecumulative probability of live birth is 68.5%. Even though the constantchance method is more conservative than the naïve chance method, themethod still over-estimates a woman's actual odds of achieving pregnancythat results in a live birth. As shown on FIG. 2, if a woman's actualprobability of achieving live birth is charted on the same graph, theestimate is even more conservative than that of the conventionalmethods. By factoring the fertility-associated phenotypic traits of thefemale subject according to the disclosed methods, aspects of theinvention are able to provide a more accurate estimation of a woman'sodds of achieving live birth.

FIG. 3 presents the same problem from a different perspective. FIG. 3tracks the rate of live birth per cycle of IVF. Under the constantchance method, the rate of live birth remains the same due to theexclusion of the women in the previous cycle who actually achievedpregnancy. Under the naïve constant chance method, however, the poolremains the same, therefore, the rate of live birth actually increasesper cycle of IVF. If the actual rate is charted on the same graph, therate of live birth decreases per cycle of IVF. That means for a womanwho did not achieve a pregnancy that resulted in a live birth after afirst round of IVF, her probability of achieving a pregnancy thatresults in a live birth actually decreases per subsequent round of IVFundertaken. Methods of the invention account for this discrepancybetween the naïve constant chance or constant chance determined ratesand the observed rate by taking into account a female subject'sfertility associated phenotypic traits to provide a more accurateestimation.

Because the women of the reference set have undergone the selectedreproductive method, information regarding the pregnancy outcome percycle of treatment is available to incorporate into the reference setdata. As mentioned earlier, assisted reproductive technologies such asIVF typically do not include a single cycle of treatment, but ratherinclude several cycles of treatment. Accordingly, knowing the pregnancyoutcome per cycle is useful.

Aspects of the invention described herein can be performed using anytype of computing device, such as a computer, that includes a processor,e.g., a central processing unit, or any combination of computing deviceswhere each device performs at least part of the process or method. Insome embodiments, systems and methods described herein may be performedwith a handheld device, e.g., a smart tablet, or a smart phone, or aspecialty device produced for the system.

Methods of the invention can be performed using software, hardware,firmware, hardwiring, or combinations of any of these. Featuresimplementing functions can also be physically located at variouspositions, including being distributed such that portions of functionsare implemented at different physical locations (e.g., imaging apparatusin one room and host workstation in another, or in separate buildings,for example, with wireless or wired connections).

Processors suitable for the execution of computer program include, byway of example, both general and special purpose microprocessors, andany one or more processor of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of computer are aprocessor for executing instructions and one or more memory devices forstoring instructions and data. Generally, a computer will also include,or be operatively coupled to receive data from or transfer data to, orboth, one or more mass storage devices for storing data, e.g., magnetic,magneto-optical disks, or optical disks. Information carriers suitablefor embodying computer program instructions and data include all formsof non-volatile memory, including by way of example semiconductor memorydevices, (e.g., EPROM, EEPROM, solid state drive (SSD), and flash memorydevices); magnetic disks, (e.g., internal hard disks or removabledisks); magneto-optical disks; and optical disks (e.g., CD and DVDdisks). The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

To provide for interaction with a user, the subject matter describedherein can be implemented on a computer having an I/O device, e.g., aCRT, LCD, LED, or projection device for displaying information to theuser and an input or output device such as a keyboard and a pointingdevice, (e.g., a mouse or a trackball), by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well. For example, feedback provided to theuser can be any form of sensory feedback, (e.g., visual feedback,auditory feedback, or tactile feedback), and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The subject matter described herein can be implemented in a computingsystem that includes a back-end component (e.g., a data server), amiddleware component (e.g., an application server), or a front-endcomponent (e.g., a client computer having a graphical user interface ora web browser through which a user can interact with an implementationof the subject matter described herein), or any combination of suchback-end, middleware, and front-end components. The components of thesystem can be interconnected through network by any form or medium ofdigital data communication, e.g., a communication network. For example,the reference set of data may be stored at a remote location and thecomputer communicates across a network to access the reference set tocompare data derived from the female subject to the reference set. Inother embodiments, however, the reference set is stored locally withinthe computer and the computer accesses the reference set within the CPUto compare subject data to the reference set. Examples of communicationnetworks include cell network (e.g., 3G or 4G), a local area network(LAN), and a wide area network (WAN), e.g., the Internet.

The subject matter described herein can be implemented as one or morecomputer program products, such as one or more computer programstangibly embodied in an information carrier (e.g., in a non-transitorycomputer-readable medium) for execution by, or to control the operationof, data processing apparatus (e.g., a programmable processor, acomputer, or multiple computers). A computer program (also known as aprogram, software, software application, app, macro, or code) can bewritten in any form of programming language, including compiled orinterpreted languages (e.g., C, C++, Perl), and it can be deployed inany form, including as a stand-alone program or as a module, component,subroutine, or other unit suitable for use in a computing environment.Systems and methods of the invention can include instructions written inany suitable programming language known in the art, including, withoutlimitation, C, C++, Perl, Java, ActiveX, HTML5, Visual Basic, orJavaScript.

A computer program does not necessarily correspond to a file. A programcan be stored in a file or a portion of file that holds other programsor data, in a single file dedicated to the program in question, or inmultiple coordinated files (e.g., files that store one or more modules,sub-programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

A file can be a digital file, for example, stored on a hard drive, SSD,CD, or other tangible, non-transitory medium. A file can be sent fromone device to another over a network (e.g., as packets being sent from aserver to a client, for example, through a Network Interface Card,modem, wireless card, or similar).

Writing a file according to the invention involves transforming atangible, non-transitory computer-readable medium, for example, byadding, removing, or rearranging particles (e.g., with a net charge ordipole moment into patterns of magnetization by read/write heads), thepatterns then representing new collocations of information aboutobjective physical phenomena desired by, and useful to, the user. Insome embodiments, writing involves a physical transformation of materialin tangible, non-transitory computer readable media (e.g., with certainoptical properties so that optical read/write devices can then read thenew and useful collocation of information, e.g., burning a CD-ROM). Insome embodiments, writing a file includes transforming a physical flashmemory apparatus such as NAND flash memory device and storinginformation by transforming physical elements in an array of memorycells made from floating-gate transistors. Methods of writing a file arewell-known in the art and, for example, can be invoked manually orautomatically by a program or by a save command from software or a writecommand from a programming language.

Suitable computing devices typically include mass memory, at least onegraphical user interface, at least one display device, and typicallyinclude communication between devices. The mass memory illustrates atype of computer-readable media, namely computer storage media. Computerstorage media may include volatile, nonvolatile, removable, andnon-removable media implemented in any method or technology for storageof information, such as computer readable instructions, data structures,program modules, or other data. Examples of computer storage mediainclude RAM, ROM, EEPROM, flash memory, or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, Radiofrequency Identification tags or chips, or anyother medium which can be used to store the desired information andwhich can be accessed by a computing device.

As one skilled in the art would recognize as necessary or best-suitedfor performance of the methods of the invention, a computer system ormachines of the invention include one or more processors (e.g., acentral processing unit (CPU) a graphics processing unit (GPU) or both),a main memory and a static memory, which communicate with each other viaa bus.

In an exemplary embodiment shown in FIG. 4, system 200 can include acomputer 249 (e.g., laptop, desktop, or tablet). The computer 249 may beconfigured to communicate across a network 209. Computer 249 includesone or more processor 259 and memory 263 as well as an input/outputmechanism 254. Where methods of the invention employ a client/serverarchitecture, an steps of methods of the invention may be performedusing server 213, which includes one or more of processor 221 and memory229, capable of obtaining data, instructions, etc., or providing resultsvia interface module 225 or providing results as a file 217. Server 213may be engaged over network 209 through computer 249 or terminal 267, orserver 213 may be directly connected to terminal 267, including one ormore processor 275 and memory 279, as well as input/output mechanism271.

System 200 or machines according to the invention may further include,for any of I/O 249, 237, or 271 a video display unit (e.g., a liquidcrystal display (LCD) or a cathode ray tube (CRT)). Computer systems ormachines according to the invention can also include an alphanumericinput device (e.g., a keyboard), a cursor control device (e.g., amouse), a disk drive unit, a signal generation device (e.g., a speaker),a touchscreen, an accelerometer, a microphone, a cellular radiofrequency antenna, and a network interface device, which can be, forexample, a network interface card (NIC), Wi-Fi card, or cellular modem.

Memory 263, 279, or 229 according to the invention can include amachine-readable medium on which is stored one or more sets ofinstructions (e.g., software) embodying any one or more of themethodologies or functions described herein. The software may alsoreside, completely or at least partially, within the main memory and/orwithin the processor during execution thereof by the computer system,the main memory and the processor also constituting machine-readablemedia. The software may further be transmitted or received over anetwork via the network interface device.

Exemplary step-by-step methods are described schematically in FIG. 5. Itwill be understood that of the methods described in FIG. 3, as well asany portion of the systems and methods disclosed herein, can beimplemented by computer, including the devices described above.Information is collected from the female subject regarding her fertilityassociated traits 301. This data is then inputted into the centralprocessing unit (CPU) of a computer 302. The CPU is coupled to a storageor memory for storing instructions for implementing methods of thepresent invention. The instructions, when executed by the CPU, cause theCPU to provide a probability of successful in vitro fertilization in aselected cycle of in vitro fertilization. The CPU provides thisdetermination by inputting the subject data into an algorithm trained ona reference set of data from a plurality of women for whomfertility-associated phenotypic traits and pregnancy outcomes for eachcycle of IVF is known 303. The reference set of data may be storedlocally within the computer, such as within the computer memory.Alternatively, the reference set may be stored in a location that isremote from the computer, such as a server. In this instance, thecomputer communicates across a network to access the reference set ofdata. The CPU then provides a probability of achieving pregnancy at aselected point in time based on the data entered into the algorithm.

INCORPORATION BY REFERENCE

References and citations to other documents, such as patents, patentapplications, patent publications, journals, books, papers, webcontents, have been made throughout this disclosure. All such documentsare hereby incorporated herein by reference in their entirety for allpurposes.

EQUIVALENTS

The invention may be embodied in other specific forms without departingfrom the spirit or essential characteristics thereof. The foregoingembodiments are therefore to be considered in all respects illustrativerather than limiting on the invention described herein. Scope of theinvention is thus indicated by the appended claims rather than by theforegoing description, and all changes which come within the meaning andrange of equivalency of the claims are therefore intended to be embracedtherein.

EXAMPLES Example 1: Reference Set

A reference set of 12,841 women (36257 cycles) was obtained thatincluded women that underwent a fertility treatment. For each woman inthe cohort, at least one of fertility-associated phenotypic traits,fertility-associated medical interventions, or pregnancy outcomes wereknown.

The reference set was divided into an algorithm development set (8640women, 24209 cycles) and a validation set (4201 women, 12048 cycles).The algorithm development set was further separated into three groupsbased on the reproductive technology used to treat the women. The firstgroup included 4,312 women (6588 cycles) that underwent in vitrofertilization (IVF; “IVF group”). Of those women, many underwent morethan one cycle of IVF, because a pregnancy that resulted in a live birthwas not achieved at the end of a first cycle of IVF. The second groupincluded 6,308 women (17940 cycles) that underwent a non-ART fertilitytreatment procedure (RE; “RE group”). Many of these women also underwentmore than one cycle of treatment. The third group included 8,640 women(24209 cycles) who received either IVF or RE treatments (“All group”).The third group was not mutually exclusive of either the first or secondgroups, (i.e., there is overlap among women in either the first andsecond group and women in the third group).

Example 2: Algorithm Development

The algorithm development set was used to train an algorithm that candetermine a female subject's probability of achieving pregnancy thatresults in a live birth at a selected point in time using a particularfertility treatment. The algorithm developed was based on a discretetime Cox proportional hazards model with time-varying covariates.Initially, indicator variables were established for categorical factors,i.e., the various fertility-associated phenotypic traits of thereference set members. Related categories were grouped whereappropriate. Those variables provided the basis on which the algorithmpredicts the likelihood of pregnancy outcome. For each variable, datawas generally truncated at approximately the 99^(th) percentile. Inaddition, missing data was accounted for prior to developing thealgorithm using imputation models derived for each numeric variablebased on subjects in the reference set with complete data. Separateimputation models were developed for RE and IVF cycles.

To select the most useful variables for training the algorithm, an L1penalized (LASSO) discrete time Cox model was used. The selectedvariables were cross-validated for accuracy and validity. The initiallist of predicting variables was then narrowed using AIC (Akaikeinformation criterion), which measured the relative goodness of fit inthe statistical model. Certain predicting variables were further droppedor combined based on manual supervision to ensure model assumptions orto make the model more stringent. Manual supervision was also used toidentify possible interaction effects between various traits, such asMale Infertility and Intrauterine Insemination. The final list ofpredicting variables for the IVF and RE reference sets are presented inFIGS. 6 and 7, respectively.

Example 3: Validation of Algorithm Using Validation Set

The algorithm was validated using the validation set by taking data(e.g., phenotypic and/or genotypic traits) of individual females fromthe reference set and running that data through the algorithm. The datawas stratified based upon reproductive technology used and within eachtechnology group, by quintile based on scores obtained for the selectedfertility associated phenotypic traits. The first quintile representsthe lowest 20% of women; the second quintile represents the 21%-40%group of women, the third quintile represents the 41% to 60% group ofwomen, the fourth quintile represents the 61% to 80% group of women, andthe fifth quintile represents the top 20% group of women.

FIG. 8 shows results by quintile for women from the reference set thatunderwent six cycles of IVF treatment. For the first quintile, theprobability of achieving a pregnancy that results in a live birth afterone cycle of IVF was approximately 15%, after two cycles of IVF wasapproximately 25%, after three cycles was approximately 30%, after fourcycles was approximately 31%, after five cycles was approximately 32%,and after six cycles was approximately 33%. For the second quintile, theprobability of achieving a pregnancy that results in a live birth afterone cycle of IVF was approximately 38%, after two cycles of IVF wasapproximately 50%, after three cycles was approximately 58%, after fourcycles was approximately 58%, after five cycles was approximately 59%,and after six cycles was approximately 59%. For the third quintile, theprobability of achieving a pregnancy that results in a live birth afterone cycle of IVF was approximately 55%, after two cycles of IVF wasapproximately 65%, after three cycles was approximately 70%, after fourcycles was approximately 71%, after five cycles was approximately 72%,and after six cycles was approximately 72%. For the fourth quintile, theprobability of achieving a pregnancy that results in a live birth afterone cycle of IVF was approximately 60%, after two cycles of IVF wasapproximately 70%, after three cycles was approximately 75%, after fourcycles was approximately 77%, after five cycles was approximately 77%,and after six cycles was approximately 77%. For the fifth quintile, theprobability of achieving a pregnancy that results in a live birth afterone cycle of IVF was approximately 65%, after two cycles of IVF wasapproximately 75%, after three cycles was approximately 80%, after fourcycles was approximately 81%, after five cycles was approximately 81%,and after six cycles was approximately 81%.

The data also show that the probability of achieving a pregnancy thatresults in a live birth for each additional cycle of IVF undergonedecreases per cycle for all five quintiles. For the first quintile, thedifference between cycle zero and cycle one is about 18%, the differencebetween cycle one and cycle two is about 7%, the difference betweencycle two and cycle three is about 5%, the difference between cyclethree and cycle four is about 1%, the difference between cycle four andcycle five is about 1%, and the difference between cycle five and cyclesix is about 0%. For the second quintile, the difference between cyclezero and cycle one is about 38%, the difference between cycle one andcycle two is about 12%, the difference between cycle two and cycle threeis about 5%, the difference between cycle three and cycle four is about1%, the difference between cycle four and cycle five is about 1%, andthe difference between cycle five and cycle six is about 0%. For thethird quintile, the difference between cycle zero and cycle one is about50%, the difference between cycle one and cycle two is about 15%, thedifference between cycle two and cycle three is about 5%, the differencebetween cycle three and cycle four is about 1%, the difference betweencycle four and cycle five is about 1%, and the difference between cyclefive and cycle six is about 0%. For the fourth quintile, the differencebetween cycle zero and cycle one is about 65%, the difference betweencycle one and cycle two is about 10%, the difference between cycle twoand cycle three is about 2%, the difference between cycle three andcycle four is about 1%, the difference between cycle four and cycle fiveis about 1%, and the difference between cycle five and cycle six isabout 1%. For the fifth quintile, the difference between cycle zero andcycle one is about 65%, the difference between cycle one and cycle twois about 10%, the difference between cycle two and cycle three is about5%, the difference between cycle three and cycle four is about 2%, thedifference between cycle four and cycle five is about 1%, and thedifference between cycle five and cycle six is about 0%.

FIG. 9 shows results by quintile for women from the reference set thatunderwent six cycles of RE treatment. For the first quintile, theprobability of achieving a pregnancy that results in a live birth afterone cycle of RE was approximately 5%, after two cycles of RE wasapproximately 10%, after three cycles was approximately 13%, after fourcycles was approximately 14%, after five cycles was approximately 15%,and after six cycles was approximately 15%. For the second quintile, theprobability of achieving a pregnancy that results in a live birth afterone cycle of RE was approximately 10%, after two cycles of RE wasapproximately 15%, after three cycles was approximately 17%, after fourcycles was approximately 18%, after five cycles was approximately 19%,and after six cycles was approximately 19%. For the third quintile, theprobability of achieving a pregnancy that results in a live birth afterone cycle of RE was approximately 12%, after two cycles of RE wasapproximately 18%, after three cycles was approximately 20%, after fourcycles was approximately 23%, after five cycles was approximately 25%,and after six cycles was approximately 28%. For the fourth quintile, theprobability of achieving a pregnancy that results in a live birth afterone cycle of RE was approximately 15%, after two cycles of RE wasapproximately 20%, after three cycles was approximately 25%, after fourcycles was approximately 30%, after five cycles was approximately 32%,and after six cycles was approximately 33%. For the fifth quintile, theprobability of achieving a pregnancy that results in a live birth afterone cycle of IVF was approximately 35%, after two cycles of RE wasapproximately 40%, after three cycles was approximately 43%, after fourcycles was approximately 48%, after five cycles was approximately 50%,and after six cycles was approximately 50%.

The data also show that the probability of achieving a pregnancy thatresults in a live birth for each additional cycle of RE undergonedecreases per cycle for all five quintiles. For the first quintile, thedifference between cycle zero and cycle one is about 5%, the differencebetween cycle one and cycle two is about 5%, the difference betweencycle two and cycle three is about 3%, the difference between cyclethree and cycle four is about 1%, the difference between cycle four andcycle five is about 1%, and the difference between cycle five and cyclesix is about 0%. For the second quintile, the difference between cyclezero and cycle one is about 10%, the difference between cycle one andcycle two is about 5%, the difference between cycle two and cycle threeis about 2%, the difference between cycle three and cycle four is about1%, the difference between cycle four and cycle five is about 1%, andthe difference between cycle five and cycle six is about 0%. For thethird quintile, the difference between cycle zero and cycle one is about12%, the difference between cycle one and cycle two is about 6%, thedifference between cycle two and cycle three is about 2%, the differencebetween cycle three and cycle four is about 3%, the difference betweencycle four and cycle five is about 2%, and the difference between cyclefive and cycle six is about 3%. For the fourth quintile, the differencebetween cycle zero and cycle one is about 15%, the difference betweencycle one and cycle two is about 5%, the difference between cycle twoand cycle three is about 5%, the difference between cycle three andcycle four is about 5%, the difference between cycle four and cycle fiveis about 3%, and the difference between cycle five and cycle six isabout 1%.

For the fifth quintile, the difference between cycle zero and cycle oneis about 35%, the difference between cycle one and cycle two is about5%, the difference between cycle two and cycle three is about 3%, thedifference between cycle three and cycle four is about 3%, thedifference between cycle four and cycle five is about 2%, and thedifference between cycle five and cycle six is about 0%.

The data show that women respond to different therapies differently andrespond to the same therapy differently. See FIGS. 8 and 9. Regardlessof therapy (IVF or RE) the cumulative probability of achieving a livebirth for each quintile increases incrementally for each additionalcycle until a plateau is reached. That benefit reaches a maximum foreach quintile to the point that the therapy being used no longerincreases the probability of achieving a pregnancy that results in alive birth. Rather, the probability remains constant regarding of thenumber of additional cycles undergone. Thus contrary to previously usedreporting methods, data herein show that at a certain point in time,continuing to undergo additional therapy cycles does not correlate withincreasing the probability of achieving a live birth.

The data also show that the type of fertility treatment used isimportant for determining a probability of achieving a pregnancy thatresults in live birth. The data show that using IVF compared to RE givesa female subject a higher probability of achieving a pregnancy thatresults in a live birth for all quintiles. See data in FIG. 8 ascompared to data in FIG. 9 for all quintiles. Additionally, the datashow that there is greater per/cycle benefit using IVF than there isusing RE. See data in FIG. 8 as compared to data in FIG. 9 for allquintiles. Thus, a single subject may be able to increase theirprobability of achieving a pregnancy that results in a live birth bychoosing the appropriate fertility treatment, and in this case,switching from RE treatments to IVF treatments. This is particularlyuseful when a woman has undergone multiple cycles of a specific therapyunsuccessfully and has reached a point that the probability of achievinga live birth using that therapy remains constant regardless of thenumber of additional cycles undergone.

Example 4: Impact of Dropouts on the Live Birth Rate

It is known that certain women stop using a fertility treatment prior toachieving a pregnancy that results in a live birth (“dropouts”), and itwas known that certain women dropped out of the reference set. As shownin FIG. 10, patients who did not achieve live birth and thendiscontinued further IVF treatment (“No.Drop”) had a lower predictedfuture success rate than patients who did not achieve live birth butcontinued treatment (“No.Stay”). Accordingly, not accounting for thedropouts leads to an overly optimistic estimation of the cumulativebirth rate.

To account for dropouts, investigations were performed using twoassumptions. The first assumption was that patients who did not returnfor further cycles of treatment had the same chance of pregnancyresulting in a live birth as those who continued treatment (optimistic).The second assumption was that patients who did not return for furthercycles of treatment had no chance of achieving live birth(conservative). The reference set was split into three groups based uponfertility treatment used (IVF group, RE group, and All group (both IVFand RE)). Based on the provided data, the algorithm determined thecumulative live-birth rate for the IVF, RE, and All group as shown inFIGS. 11-13, respectively. The cumulative birth rate was determinedunder both optimistic and conservative assumptions regarding patientswho dropped out of the study.

FIG. 11 shows optimistic and conservative results for women from thereference set that underwent IVF treatment. As shown in FIG. 11, theconservative and optimistic birth rates were approximately 45% after onecycle of IVF. After cycle 1, the conservative and the optimistic birthrates diverge, with the optimistic birth rate having a higherprobability of achieving a live birth for each additional cycle than theconservative birth rates. The divergence continuously increased witheach additional cycle. FIG. 12 shows optimistic and conservative resultsfor women from the reference set that underwent RE treatment. As shownin FIG. 12, the conservative and optimistic cumulative live birth rateswere roughly 15% after one cycle of RE. After cycle 1, the conservativeand the optimistic birth rates diverge, with the optimistic birth ratehaving a higher probability of achieving a live birth for eachadditional cycle than the conservative birth rates. The divergencecontinuously increased with each additional cycle. FIG. 13 showsoptimistic and conservative results for women from the reference setthat underwent both IVF and RE treatments. FIG. 13 shows that theconservative and optimistic live birth rates were 20% for All groupsafter one cycle. After cycle 1, the conservative and the optimisticbirth rates diverge, with the optimistic birth rate having a higherprobability of achieving a live birth for each additional cycle than theconservative birth rates. The divergence continuously increased witheach additional cycle.

The data shows that dramatically different results are obtained based onthe assumptions made about the women that dropped out of the referenceset. The assumption that patients who did not return for further cyclesof treatment had the same chance of pregnancy resulting in a live birthas those who continued treatment is overly optimistic and results inreporting a per cycle probability higher than would be expected. Theassumption that patients who did not return for further cycles oftreatment that patients who did not return for further cycles oftreatment had no chance of achieving live birth is overly pessimisticand results in reporting a per cycle probability lower than would beexpected. The data illustrate that dropouts must be accounted for toaccurately report a female's probability of achieving a pregnancy thatresults in a live birth at a selected point in time using a particularfertility treatment

Example 5: Algorithm that Accounts for Dropouts

Dropout models were developed for each of the groups within thereference set (IVF, RE, and All) and subsequently used to train thealgorithm. The dropout model estimates the likelihood of dropout foreach subject at each cycle. The subjects who do not dropout are weightedproportionally to the likelihood that they would have dropped out.Accounting for the dropouts in this manner attempts to preserve thepopulation characteristics in the analysis with the progression of time.

Models were built using logistic regression statistical methods. Thedropout models were then used in conjunction with Inverse Probability ofCensoring Weighting (IPCW) methods to adjust the clinical models orKaplan-Meier curves based on the reference data from an overlyoptimistic assessment to a more accurate determination of live birth. Ass simple example of developing the dropout model, assume 30% of thesubjects in the reference set have high levels of follicle stimulatinghormone (FSH). After cycle 1, assume 50% of the subjects with high FSHdrop out, i.e., discontinue treatment. Without weights, the populationat cycle 2 would only be 15% high FSH. To account for the dropouts, forcycle 2, assign a weight of 2 for a high FSH subject still in the studyand a weight of 1 for a low FSH subject still in the study. Withweights, the population at cycle 2 is 30% FSH (i.e., the high FSHsubjects count twice), which is the same as if dropout had not occurred.Since the influence of a subject in the model is proportional to theweight, mathematically speaking, the model coefficients are correctedfor bias induced by the non-random dropout.

Example 6: Validation of Algorithm that Accounts for Dropouts UsingValidation Set

The Algorithm that accounts for dropouts was then validated using thevalidation set by taking data (e.g., phenotypic and/or genotypic traits)of individual females from the validation set and running that datathrough the algorithm. The data was stratified based upon reproductivetechnology used. FIGS. 14-16 present adjusted birth rates that accountfor dropouts in the various reference sets. Optimistic and conservativerates are also provided along with the adjusted rate. As shown in FIG.16, the cumulative live birth rate using IVF is approximately the samefor the first two cycles of treatment using optimistic, conservative, or“adjusted” methods. As cycles progress, however, the adjusted birth rateis observed to be between the optimistic and conservative birth rate.Similar trends are observed for the RE and All groups, as shown in FIGS.15-16, respectively. Good predictive accuracy was observed for all threemodels based on AUC (Area Under Curve) analysis. IVF model AUC wasdetermined to be 0.71±0.02; the RE Model AUC was determined to be0.73±0.02; and the All Model AUC was determined to be 0.81±0.02.

As FIGS. 14-16 demonstrate, the more accurate assessment of a subject'sprobability for achieving a live birth does not follow the optimisticrate or the conservative rate but lies between those rates respectively.Accordingly, using the methods disclosed herein, one can determine aprobability of achieving a pregnancy at a selected point in time withgreater accuracy and confidence than previously used algorithms.

Example 7: Identification of a Subset of Factors Leading to Dropout

Example 6 above discusses the importance of accounting for dropouts whendetermining a subject's probability for achieving a live birth. ThisExample determines specific factors leading to such dropouts (e.g.,treatment discontinuation). A relatively large multi-year cohort studywas conducted in which variables (also referred to as “factors”) thatwere most critical in contributing to dropout were identified andassessments were made as to whether such patients discontinued treatmentprematurely based on one or more of the identified factors.

In the study, data was collected from a set of ART patients. Inparticular, the data included retrospective de-identified data fromapproximately 6,396 ART patients (>10,000 cycles and several hundredclinical variables) aged 21 or greater from an academic reproductivemedicine center who had at least one cycle with a fresh or frozen embryotransfer. The clinical variables included, for example, demographics,lab assessments and numbers, treatments, oocyte/embryo measure,insurance information, etc. The variables were assessed usingmultivariate logistic regression, adjusting for likelihood of livebirth, number of treatment cycles, and financial status. A subset offactors that were determined to be involved in contributing to dropoutare included in Table 3. This is not an exclusive list, and otherfactors, such as those described in Example 6, also contribute todrop-out. However, without being limited by any particular theory ormechanism of action, the factors identified in Table 3 may play a largerrole than other factors.

TABLE 3 Identified Subset of Factors Related to Dropout Factor OddsRatio P-value Predicted Likelihood of Live Birth for Next Cycle* 0.45<.0001 At Least 1 Embryo Cryo-preserved during Cycle* 0.34 <.0001 CycleNumber 1.27 <.0001 Down regulation stimulation 0.69 <.0001 HealthInsurance Coverage* 0.83 .0002 Prior Full Term Birth 1.27 .0002 Flarestimulation 2.29 .006 Oral contraceptive adjunct 0.81 .024 Diagnosis ofHabitual Loss 0.65 .035

In contrast to previous studies that assume patient dropout is random orbased on failed cycles/poor prognosis, this study identified clinicalfactors significantly associated with dropout. The identified factorswere used in a multivariate model to project when ART success ratesplateau. It was found that patients (mean age 36.6±5.2) achieved a livebirth after an average of 3 cycles, but dropout averaged 27% per cycle.Factors such as lack of cryopreserved embryos (Odds Ratio (OR)=2.91,p<0.001), full term births prior to ART (OR=1.26, p<0.001), and flarestimulation protocol (OR=2.29, p=0.006) significantly increased dropoutrate. Conversely, down-regulation stimulation (OR=0.69, p<0.001), use ofan oral contraceptive as adjunct to treatment (OR=0.81, p=0.024), anddiagnosis of habitual loss (OR=0.65, p=0.037) significantly decreaseddropout rate. It was further found that 48% of patients, clinicallysimilar to dropouts, who chose to continue treatment, achieved livebirth within two additional cycles (42% for women >38 years of age).

The results show that patients typically discontinue ART too early asdropout-adjusted success rates do not appear to plateau until cycle 5.This study suggests that couples should not be discouraged by initialfailed cycle attempts. Even women >38 years of age with failed cycleshad high success rates within as few as 2 cycles following a failedcycle.

What is claimed is:
 1. A system for determining a probability ofachieving a pregnancy at a selected point in time, the systemcomprising: a central processing unit (CPU); and storage coupled to saidCPU for storing instructions that when executed by the CPU cause the CPUto: accept as input, data representative of a plurality offertility-associated phenotypic traits of a female subject; analyze theinput data using a prognosis predictor generated by: obtaining areference set of data from a plurality of women across one or morereproductive cycles, the reference set of data comprisingfertility-associated phenotypic traits and known pregnancy outcomes,wherein the plurality of women accounts for a dropout population ofwomen who ceased pregnancy attempts prior to reaching a live birthoutcome and a non-dropout population of women who continued pregnancyattempts until live birth; determining one or more correlations betweenthe at least one of the fertility-associated phenotypic traits and theknown pregnancy outcomes at a plurality of time points across thereproductive cycles, wherein the dropout population is accounted for byestimating a likelihood of dropout for each subject at each reproductivecycle and weighing the women in the non-dropout populationproportionally to the likelihood of dropout; and training the prognosispredictor on the reference set of data; provide a probability ofachieving a pregnancy for the female subject at a selected point in timeas a result of running the prognosis predictor on said input data; andtreating the female subject with a fertility treatment based on theprobability of achieving a pregnancy.
 2. The system of claim 1, whereinthe probability of achieving pregnancy comprises the probability ofachieving pregnancy that results in a live birth.
 3. The system of claim1, wherein the correlations for said fertility-associated phenotypictraits are adjusted at each time point across the plurality ofreproductive cycles.
 4. The system of claim 1, wherein said input dataare obtained from at least one selected from the group consisting of aquestionnaire, a medical history of said female subject, a familymedical history of said female subject, and a combination thereof. 5.The system of claim 1, wherein said data are obtained by analyzing asample collected from a person selected from the group consisting of:said female subject, intimate partners of said female subject,blood-related relatives of said female subject, gamete donors, embryodonors, gestational carriers, and a combination thereof.
 6. The systemof claim 5, wherein said sample is a human tissue or bodily fluid. 7.The system of claim 1, wherein the input data of the female subjectfurther comprises fertility-associated genotypic traits of the femalesubject.
 8. The system of claim 7, wherein the reference set of datafrom the plurality of women further comprises fertility-associatedgenotypic traits.
 9. The system of claim 7, wherein the genotypic traitsof the subject are obtained by conducting an assay on a sample from thesubject to determine the presence or absence of a genetic variation thatis associated with infertility.
 10. The system according to claim 9,wherein the assay is selected from the group consisting of sequencing,hybridization to an array, and an amplification reaction.
 11. The systemof claim 9, wherein the genetic variation is selected from the groupconsisting of a single nucleotide polymorphism, a deletion, aninsertion, a rearrangement, a copy number variation, and a combinationthereof.
 12. The system of claim 7, wherein the genotype traits arereflective of expression levels of one or more fertility-associatedgenes.
 13. The system of claim 1, wherein the fertility-associatedphenotypic trait is a trait selected from Table
 1. 14. The system ofclaim 1, wherein the prognosis predictor is stored at a remote locationand the CPU communicates across a network to access said prognosispredictor.
 15. The system of claim 1, wherein the prognosis predictor isstored locally within the CPU and the CPU accesses the prognosispredictor within the CPU.
 16. The system of claim 1, wherein the datafurther comprises a plurality of fertility-associated phenotypic traitsof a male subject.
 17. A method for determining a probability ofachieving a pregnancy at a selected point in time, the methodcomprising: accepting as input, data representative of a plurality offertility-associated phenotypic traits of a female subject; analyzingthe input data using a prognosis predictor generated by: obtaining areference set of data from a plurality of women across one or morereproductive cycles, the reference set of data comprisingfertility-associated phenotypic traits and known pregnancy outcomes,wherein the plurality of women accounts for a dropout population ofwomen who ceased pregnancy attempts prior to reaching a live birthoutcome and a non-dropout population of women who continued pregnancyattempts until live birth; determining one or more correlations betweenthe at least one of the fertility-associated phenotypic traits and theknown pregnancy outcomes at a plurality of time points across thereproductive cycles, wherein the dropout population is accounted for byestimating a likelihood of dropout for each subject at each reproductivecycle and weighing the women in the non-dropout populationproportionally to the likelihood of dropout; and training the prognosispredictor on the reference set of data; providing a probability ofachieving a pregnancy for the female subject at a selected point in timeas a result of running the prognosis predictor on said input data; andtreating the female subject with a fertility treatment based on theprobability of achieving a pregnancy.
 18. The method of claim 17,wherein the probability of achieving pregnancy comprises the probabilityof achieving pregnancy that results in a live birth.
 19. The method ofclaim 17, wherein the correlations for said fertility-associatedphenotypic traits are adjusted at each time point across the pluralityof reproductive cycles.
 20. The method of claim 17, wherein said inputdata are obtained from at least one selected from the group consistingof a questionnaire, a medical history of said female subject, a familymedical history of said female subject, and a combination thereof. 21.The method of claim 17, wherein said data are obtained by analyzing asample collected from a person selected from the group consisting of:said female subject, intimate partners of said female subject,blood-related relatives of said female subject, gamete donors, embryodonors, gestational carriers, and a combination thereof.
 22. The methodof claim 21, wherein said sample is a human tissue or bodily fluid. 23.The method of claim 17, wherein the input data of the female subjectfurther comprises fertility-associated genotypic traits of the femalesubject.
 24. The method of claim 23, wherein the reference set of datafrom the plurality of women further comprises fertility-associatedgenotypic traits.
 25. The method of claim 23, wherein the genotypictraits of the subject are obtained by conducting an assay on a samplefrom the subject to determine the presence or absence of a geneticvariation that is associated with infertility.
 26. The method of claim25, wherein the assay is selected from the group consisting of:sequencing, hybridization to an array, and an amplification reaction.27. The method of claim 26, wherein the genetic variation is selectedfrom the group consisting of: a single nucleotide polymorphism, adeletion, an insertion, a rearrangement, a copy number variation, and acombination thereof.
 28. The method of claim 23, wherein the genotypetraits are reflective of expression levels of one or morefertility-associated genes.
 29. The method of claim 17, wherein thefertility-associated phenotypic trait is a trait selected from Table 1.30. The method of claim 17, wherein the data further comprises aplurality of fertility-associated phenotypic traits of a male subject.31. A method for determining a probability of achieving a pregnancy at aselected point in time, the method comprising, the method comprising:inputting data representative of a plurality of fertility-associatedphenotypic traits of a female subject into a computer; causing thecomputer to: analyze the input data using a prognosis predictorgenerated by: obtaining a reference set of data from a plurality ofwomen across one or more reproductive cycles, the reference set of datacomprising fertility-associated phenotypic traits and known pregnancyoutcomes, wherein the plurality of women accounts for a dropoutpopulation of women who ceased pregnancy attempts prior to reaching alive birth outcome and a non-dropout population of women who continuedpregnancy attempts until live birth; determining one or morecorrelations between the at least one of the fertility-associatedphenotypic traits and the known pregnancy outcomes at a plurality oftime points across the reproductive cycles, wherein the dropoutpopulation is accounted for by estimating a likelihood of dropout foreach subject at each reproductive cycle and weighing the women in thenon-dropout population proportionally to the likelihood of dropout; andtraining the prognosis predictor on the reference set of data; andprovide a probability of achieving a pregnancy for the female subject ata selected point in time as a result of running an algorithm on theinputted data; and treating the female subject with a fertilitytreatment based on the probability of achieving a pregnancy.
 32. Themethod of claim 31, wherein the probability of achieving pregnancycomprises the probability of achieving pregnancy that results in a livebirth.
 33. The method of claim 31, wherein the correlations for saidfertility-associated phenotypic traits are adjusted at each time pointacross the plurality of reproductive cycles.
 34. The method of claim 33,wherein both the reference set of data from the plurality of women andthe inputted data from the female subject further comprisesfertility-associated genotypic traits.
 35. The method of claim 33,wherein the genotypic traits of at least one of the subject and theplurality of women from the reference set of data are obtained byconducting an assay on a sample from the subject to determine thepresence or absence of a genetic variation that is associated withinfertility.
 36. The method of claim 31, wherein the prognosis predictoris stored at a remote location and the computer communicates across anetwork to access said prognosis predictor.
 37. The method of claim 31,wherein the prognosis predictor is stored locally within the computerand the computer accesses the prognosis predictor within the computer.38. The method of claim 31, wherein the data further comprises aplurality of fertility-associated phenotypic traits of a male subject.